N=1: n of small

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 
N=1: Symptom vs. Syndrome
N=1: Latency and Half-Life

The biggest limitation of an N=1 experiment is external validity. If you run enough trials on yourself, you can show that some intervention does or doesn’t have an effect on you to basically any degree of certainty that you want. But this will never provide much evidence that the same intervention will have the same effect, or any effect, on anyone else. 

People are all human and have roughly the same human biology, it’s true. In the higher animals, decapitation is more or less guaranteed to be lethal; people generally like eating sugar and hate eating asphalt. But once you move beyond the fundamentals of biology, most other bets quickly are off. 

An unspoken assumption of the self-experiment discussion (including our posts on the subject) is that there are exactly two kinds of research — self-experiments, and large trials. These occupy the sample size slices of N = 1 and N ≥ 30, respectively. The self-experiment and case study are assumed to be a single subject; and with few exceptions, most people don’t trust a survey or RCT with anything less than 30 participants. 

But there are two problems with this perspective. The first is that this is a false dichotomy. There isn’t a point where N = 1 turns into N = small, and there’s no sample size where you go from having a collection of case studies to having a trial. Going from N = 29 to N = 30 does nothing in particular, and there is no other threshold that stands out as being at all distinct (except N = 0 to N = 1, of course). A bigger sample size always means more information and better external validity, with no discontinuity.

The second problem is that if N = 1 is at all good (and we think that it is), then N of small has to be better. 

Anything that is good with an N of 1 will be better with an N of 2-10. With N of small, you get more data, more quickly. One person doing random daily trials over the course of a week will create 7 data points. Three people doing random daily trials over the course of a week will create 21 data points. Small-group analysis is a little more complicated, but the data can be handled by a standard linear mixed model (here’s an example that involves dragons). 

With N of small, you get more diversity of participants and more diversity of responses, quickly drawing the fangs from the problem of external validity. You will be able to get some sense of whether the intervention works differently for different people. If you have five participants, it will be easy to see if they are all responding the exact same way, if they are responding somewhat differently, or if some of them are having huge responses while others feel nothing at all. 

The only question is one of cost. Because while the biggest limitation of N = 1 is external validity, the biggest benefit is that it’s cheap in important ways. With N = 1, you don’t need anyone’s permission to start your study — you can just go do it. You don’t pay any coordination costs, costs which are easy to miss up front but can be quite a drag if you’re not careful. These factors help make self-experiments cheap. 

But we think scaling up is usually worth it — or at least, once you have some promising N = 1, scaling to N of small usually makes sense. It’s the logical next step. And since there’s no real distinction between a single case study, a small collection of case studies, and a trial of 100 people, it’s also the logical next step on the path towards an RCT or other large trial. 

So while this series has focused on true N = 1 self-experiments, the real wins for the future may be in N = 2-10 studies where people grab a couple of friends and run a self-experiment together. Remember kids, friendship is the most powerful force in the universe

And it’s not at all unprecedented, since this is how we approached our community trials; we looked at a couple of case studies, and then used N of small to do the pilot testing. 

For the potato diet, we started with case studies like Andrew Taylor and Penn Jilette; we recruited some friends to try nothing but potatoes for several days; and one of the SMTM authors tried the all-potato diet for a couple weeks. 

For the potassium trial, two SMTM hive mind members tried the low-dose potassium protocol for a couple of weeks and lost weight without any negative side effects. Then we got a couple of friends to try it for just a couple of days to make sure that there weren’t any side effects for them either. 

For the half-tato diet, we didn’t explicitly organize things this way, but we looked at three very similar case studies that, taken together, are essentially an N = 3 pilot of the half-tato diet protocol. No idea if the half-tato effect will generalize beyond Nicky Case and M, but the fact that it generalizes between them is pretty interesting. We also happened to know about a couple of other friends who had also tried versions of the half-tato diet with good results. 

We think that in all of these cases, N of small was much more convincing than N = 1 would have been. With two people, it’s much less likely that the effect is a fluke. Even if it works for one person and not for the other, that’s still evidence that we shouldn’t expect the effect to be entirely consistent; we should expect more ambiguity. And for something where the risks are unclear, like with potassium, two people going through without any side-effects is much more reassuring than one. 

N=1: Latency and Half-Life

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 
N=1: Symptom vs. Syndrome

I. Latency

a. Melons

Peter has a bad reaction to melons. Every time he eats melon, he gets sick right away, and he often throws up. 

We can say that Peter’s reaction to melon has low latency. When it happens, it happens right away. No waiting about.

Mark also has a bad reaction to melons. But because of a complex series of biochemical interactions, when Mark eats melon, he doesn’t get sick right away. He gets sick about three days (72 hours) later, when he suddenly starts to feel very ill, and then often throws up.

We can say that Mark’s reaction to melon has high latency. It happens, but it always takes a long time to kick in.

Peter and Mark have basically the same reaction to melon. Both have the same symptoms — nausea, sickness, and vomiting. Both reactions happen for sure every time — they are both equally reliable. The only thing that’s different is the latency.


b. Different and the Same

Though their reactions are nearly identical, Peter and Mark end up with very different experiences of their sensitivity. 

Peter quickly learns that melon is a trigger. After all, he gets sick right away. He just makes sure to avoid melon and goes about his life with no additional air of mystery. 

Mark, on the other hand, is plagued with random, crippling nausea. He sometimes gets sick, and it always seems to be for no reason. This is because it’s hard to remember what you were eating exactly 72 hours ago (for example, take a moment to try to remember what YOU were eating 72 hours ago). So for Mark, the connection is very obscure. He may never figure it out.

Both of these relationships would become equally obvious in a self-experiment. As long as you were tracking melon consumption and looking for relationships over a long enough time frame, you would see that Peter gets sick right after every dose of melon, and Mark gets sick exactly 72 hours after every dose of melon. 

Perfect 100% reliability would make this pretty obvious once you noticed it. You don’t need a huge sample size to pick up on a relationship that is 100% reliable, which is why Peter quits melons after getting sick just a few times. 

The big difference is whether the relationship jumps out at you or not. Low-latency relationships are obvious; the close proximity of cause and effect highlights the correct hypothesis and draws immediate attention to the relationship, where it can quickly be confirmed. Peter can just eat more melon and immediately get corroborating evidence if he wants to confirm his theory. The relationship is intuitive; you know it when you see it. 

c. Cause and Effect

High-latency relationships are much harder to spot, even if they are equally reliable. The separation of cause and effect means that the connection may never come to mind. 

To even be able to pick up on this in a self-experiment, you would have to know in advance that you should be tracking how much melon you are eating. And this is the hard part. The hard part is not demonstrating the relationship. At 100% reliability, that’s easy. The hard part is picking up on what to track. 

This is somewhat in contrast to our normal concerns in research. Normally we worry about sample size and the quality of our measures. But Mark doesn’t need a big sample size. He doesn’t need any measures other than “got sick” and “ate melon”. All he needs is to consider melon as a possible cause of his nausea, and to consider looking for relationships with a latency of at least 72 hours. Easier said than done. 

d. Reliability in Real-World Relationships

Of course, most real-world relationships are not 100% reliable. Few things work every time. But it’s concerning how a little latency can hide an otherwise blatant relationship, and it makes us wonder how many connections we all miss because of relatively small delays in onset. 

Zero latency (eat melon, immediately puke) is easy to figure out. These relationships become obvious after just a few trials. 

In comparison, 72-hour latency is very hard to figure out. Most people are not looking for relationships with such a long delay, and even if you were, you would be hard pressed to figure out the cause. 

You can’t just keep a food journal and look 72 hours back — you don’t know how long the latency is, so you don’t know how far back to look! And if the latency varies at all (e.g. always between 60-80 hours later), it gets even harder.

This makes us wonder how much latency we can handle before connections stop being obvious. It may not take much. Coffee -> heartburn with an hour delay, that seems pretty doable. We think you would figure that one out pretty quickly. But with a four hour delay? Eight hours? Twelve? This would be much more difficult. It would start to look more like, “heartburn around dinnertime / going to bed, especially on weekdays”. That sounds hard to puzzle out. 

Latency also makes it harder to get a big sample size. With a latency of less than 5 minutes, Peter can easily do eight trials (eat some melon and face the consequences) in a single day. Mark can’t do that. He has to wait 72 hours to get the results from his first trial, except it’s worse than that, because he doesn’t know how long he has to wait for the results to come in. 

If he wants to make sure not to cross the streams, he needs to devote three whole days (though again, he doesn’t actually know in advance how much time he has to dedicate) to each trial, so he needs 3 * 8 = 24 days to do the same number of “eat melon and find out” trials that Peter can easily do in an afternoon, if he’s willing to get sick that much in a single day.

II. Half-Life

a. Creamer

Jo has a bad reaction to one of the additives in her office’s tiny cups of dairy creamer (henceforth: “creamer”). Every time she uses one of the tiny cups, she gets very tired about 30 minutes later. Fortunately, Jo’s kidneys happen to handle the additive really well, and two hours after she takes the creamer, she has cleared all of the additive out of her system, and stops feeling unusually tired. 

We can say that the additive has a short half-life in Jo’s system, and that the symptoms (fatigue) have a short half-life as well. They don’t stick around for long, things quickly go back to baseline. 

Lily works in the same office and has the exact same reaction to the same additive in the office’s tiny cups of dairy creamer. Every time she uses one of the tiny cups, she gets very tired about 30 minutes later. But through a random accident of biology, Lily’s body doesn’t clear the additive from her system nearly as quickly as Jo’s does. The additive sticks around for a long time, and Lily keeps feeling tired all week. If she takes some creamer on a Monday, she’s just getting over it on Sunday afternoon. 

We can say that the additive has a long half-life in Lily’s system, and that the symptoms (fatigue) have a long half-life as well. They stick around for a long-ass time, and it takes forever for her to feel normal again.

b. Puzzling it Out

Much like a long latency, a long half-life makes this problem much harder to puzzle out, even when the two cases are otherwise identical.

Jo has it easy. If she comes to suspect the creamer, she has a lot of options. She can try taking creamer some mornings and not other mornings. She can try taking the creamer at different times of day and seeing if the fatigue also kicks in at different times. She can even take the creamer multiple times in the same day. Since the symptoms clear out after just two hours, she’s quickly back to baseline and is ready for another trial. If she wants to compare different brands of creamer to see if there’s a difference, she can get a pretty good sample size in a weekend. It’s easy for her to collect lots of data.

Lily has it really hard. If she comes to suspect the creamer, she is in a real bind, and most of the traps are invisible. If she tries taking the creamer some mornings and not other mornings, her results will be a mess, because as soon as she takes it one morning, she is fatigued all week. It will look like the creamer has no effect at all, since on days when she doesn’t take the creamer, she is still fatigued from any creamer she took in any of the previous seven days. A day-by-day self-experiment would show no effect, even though this is totally the wrong conclusion.

To detect any effect, Lily needs to test things in blocks of weeks, instead of blocks of days or hours. Each Monday, either take the creamer or not, and see how tired she is that week. But you can see how hard it would be for her to figure out this design — how is she supposed to know in advance that she needs to study this problem in blocks of a full week? She has a lot less flexibility; you might say that her research situation is much less forgiving. 


Even if Lily does pin down the right research design, it still takes her much longer to get the same amount of data. Randomly assigning creamer or no creamer each morning, Jo can get 28 data points in four weeks, which is enough data to detect a strong relationship if there is one. Meanwhile, in four weeks Lily would get only four datapoints, not enough to be at all convincing. 

If the relationship is weaker (e.g. only a 50% chance of becoming fatigued), things are even worse. Jo can get a sample size of 100 or 200 days if she has to; it would be a pain, but she could make it happen. But for Lily to get a sample size of 100 weeks would take two years.

c. Thought it Worked for a While 🙂 

Lots of people try something, feel like it works great, and then later when they do a more rigorous self-experiment or just keep trying it, they feel that the effect wears off. Must have just been excitement over trying a new thing. 

For example, back in early 2020 Scott Alexander put out a report describing his experience with Sleep Support, a new (at the time) product by Nootropics Depot. His sleep quality isn’t great, so he decided to give this new supplement a shot, and reported miraculous results: 

The first night I took it, I woke up naturally at 9 the next morning, with no desire to go back to sleep. This has never happened before. It shocked me. And the next morning, the same thing happened. I started recommending the supplement to all my friends, some of whom also reported good results.

“I decided the next step was to do a randomized controlled trial,” he says. To make a long story short, the RCT found no difference at all in any measure of sleep quality. “My conclusion is that the effect I thought that I observed – a consistent change of two hours in my otherwise stable wake-up time – wasn’t real. This shocked me. What’s going on?”

Scott chalks this up to the placebo effect, which is certainly possible. But another possibility is that Sleep Support did work great at first but was no longer detectable (for whatever reason) by the time he set up the RCT. Obviously if this is true, it would be hard to study; but it does perfectly match Scott’s experience, which is otherwise (as he says) shocking and somewhat confusing.

If you have any experience with chronic illness or biohacking or anything similar, then you know that “thought it worked for a while” is a very common story. When this happens, the assumption is usually that you were fooling yourself the first time around. But consider:

Vitamin C cures scurvy, so if you have scurvy, the first few doses of vitamin C are great! But after that, vitamin C has basically no effect, because you no longer have scurvy. You have been cured. Looking at this data (huge increases in wellbeing on the first few days, but after that, nothing), the research team concludes that the original reports were somehow mistaken. 

No! It’s just that the vitamin C helped and then it had done all it could! It had a huge effect! That effect was just all up front! 

This exact scenario should pop up all over the place. If you are iron deficient, the first few doses of iron will have some effect. After that, they will have no effect. If you are B12 deficient, the first few doses of B12 will have some effect. After that, they will have no effect. Et cetera.

This is because the body is able to keep reserves of all of these substances. As long as you’ve been getting enough vitamin C, you can go for 4 weeks without any vitamin C at all before you start getting scurvy (in reality it usually takes more like 3 months, because most people don’t go entirely cold turkey on vitamin C). Same goes for iron and B12 — your body is able to keep reserves of these substances, so as long as you get enough, you should be set for a while.

To put this back in the terms of this essay, we would say that these positive effects have a long half-life. Positive effects with a long-half life face exactly the same issues as negative effects with a long-half life — you have to make sure you take the half-life into account when designing a study, and use long enough study periods, otherwise your data will be confused and misleading.

This same point applies to a lot of treatments, actually. Assuming you have an infection, antibiotics will show a big effect up front and then nothing after that. But we don’t take this to mean that antibiotics have no effect, oops we thought it worked for a while, guess we were wrong.

This isn’t a problem for things with no reservoir. For example, as far as we can gather, zinc isn’t really stored in the body long-term. So most effects of zinc will (probably) have a short half-life. If you need more zinc, you can just take it on a given day and see the effects.  

Supplementing anything with a large reservoir (or other positive effect with a long half-life) may not be suitable for a self-experiment, because it will show a strong effect in the first few days and no effect after that. Aggregated over 30 days or whatever, this will look like no effect or a weak effect. Clearly this is the wrong interpretation.

And the longer you run the self-experiment for, the smaller the effect will appear! If you do a 10-day self-experiment with antibiotics, and they have an effect on the first two days, then you will find that this looks like 2/10 days show an effect, which will probably average out to a small effect. But if you kept going for 100 days, you would see that 2/100 days show an effect, which will average out to basically no effect at all.

This is the opposite of our normal assumption about sample sizes, that a larger sample size will always get us a more meaningful, accurate estimate. This assumption simply isn’t true if we’re dealing with a treatment that has a long half-life. 

So consider the half-life of positive effects too.


Broadly speaking, triggers have some delay in the onset of their symptoms, and those symptoms stick around for some span of time. 

Having a high latency or a long half-life makes a relationship much harder to notice, and harder to study. Having both, it gets even worse.

Perhaps Bob is allergic to dairy, or whatever. It gives him hives, but with a latency of two days, and they persist for four days. Bob will be walking around with random hives, and not much hope of finding out why. 

He might come to suspect the true cause if he happens to cut out dairy for a while and the hives go away for good. But if someone challenged him on this — or if Bob, being a good scientist, decided he wanted to run a self-experiment to demonstrate the hive-causing effect — he would be hard pressed to get convincing formal evidence. 

Bob wouldn’t know in advance to look for a latency of two days and persistence of four days. If he did something reasonable, like randomly assign each day as dairy or non-dairy, the results would look like zero effect. On most days when he took no dairy, he would have hives anyways, because of the long half-life. On most days when he did take dairy, he would also have hives, because they stick around so long. The few “no hive” days would be in the random periods where he hadn’t had any dairy several days ago; but those days might well be days when he was assigned to drink dairy. So it would look like a wash, even though it’s actually a very reliable relationship. 

Bob would have to do something that seems totally unreasonable, like structure the trial in 6-day segments to account for these delays. If he did this right, the 2-day wait and 4-day stay would become entirely obvious. But how is he supposed to know in advance that he has to use this totally weird study design? 

N=1: Symptom vs. Syndrome

Previously in this series:

N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 


People like to argue about whether obesity is a disease. Does it require treatment, or is it more of a social problem? But obesity isn’t a disease. It’s clearly a symptom. 

Think about it like this. Fatigue is a symptom, and it’s a symptom of many things. Fatigue can be a symptom of everyday decisions — you can be fatigued because you stayed up until 3 AM last night playing Octodad: Dadliest Catch. It can be a symptom of substances, like alcohol or Benadryl. It can be a symptom of conditions, like anemia or concussion. And fatigue can be a symptom of diseases, like mononucleosis, Parkinson’s, or lupus. 

Similarly, a person can be obese for a number of different reasons. Obesity is a symptom of many different conditions. You can be obese because of a brain injury. You can be obese because of a thyroid issue. You can be obese because you’re taking a drug like haloperidol or olanzapine. And while there’s still a lot of dispute over the source of the global obesity epidemic, you can be obese because of whatever cause(s) are causing that. 


Things get confusing when you try to treat a symptom like a disease. 

Think about fatigue. If your friend is tired from playing video games until the wee hours of the morning, the correct treatment is for them to play video games while pretending to fill out spreadsheets at work, like a normal person. If they’re fatigued from drinking merlot or taking Benadryl, the only real option is to have them wait until the drug wears off (or take an upper, but that’s not really recommended). If they’re anemic, then they need to get more iron. Et cetera.

Similarly, we don’t know how to treat the general obesity we see in the obesity epidemic. But we do have treatments for obesity caused by thyroid disorders or brain tumors. And we shouldn’t be shocked if treatments that work for obesity caused by thyroid disorders don’t work for the obesity caused by brain tumors, or don’t work for the widespread obesity we see today.

Because a symptom can have many different causes, just looking at the symptom won’t always tell you the cause. And if you don’t know the cause, then you may not know the right treatment, because you don’t know the etiology; you don’t know how the cause connects to the symptom, at what points you can intervene, and what kinds of interventions might be helpful.

This is pretty bad — even when there’s a finite list of possible causes, it’s hard to look at a symptom and figure out which of its causes are responsible. 


Many chronic illness symptoms are nonspecific. Per Wikipedia

Nonspecific symptoms are very general and thus can be associated with a wide range of conditions. In other words, they are not specific to (not particular to) any one condition. Most signs and symptoms are at least somewhat nonspecific, as only pathognomonic ones are highly specific. But certain nonspecific signs and symptoms are especially nonspecific and especially common. They are also known as constitutional symptoms when they affect the sense of well-being. They include unexplained weight loss, headache, pain, fatigue, loss of appetite, night sweats, and malaise.

This means that people who are diagnosed with the same chronic illness could have similar experiences, similar symptoms, with entirely different causes. If you have headache/pain/fatigue, you might reasonably assume that someone else with headache/pain/fatigue has the same illness, and that it was caused by the same thing. You might assume that the same treatments will work for both of you, that your illness would have the same cure. 

But headache/pain/fatigue are all nonspecific — they can all be caused by a zillion [sic] different things. So someone who shares your exact symptoms may have the exact same experience but for totally different reasons. If this is the case, the treatments that work for one of you may not help at all for the other.

(Even worse, palliative treatments will tend to work for both of you, since they treat the symptoms directly, and this will make the two conditions seem even more similar. But curative treatments that work for one of you won’t work for the other, since your conditions have different root causes.)

Let’s consider migraines. Migraines can definitely be caused by hormones. Some people have migraines only during certain parts of their period (about 7-14% of women, according to Wikipedia), or only when pregnant. Migraines can also be caused, or at least partially caused, by triggers like stress or certain foods.

But there are also people who get random mystery migraines on a regular basis, with no apparent trigger. Presumably these are caused by something, but it’s not something obvious like stress or hormonal cycles or being pregnant. So clearly migraines are a symptom, not a disease — they can be caused by several different things.

All this to say that finding the “cause” of migraines may be the wrong framing. There may be no more single cause of migraines than there is a single cause of car accidents. Some accidents happen because the driver wasn’t paying attention (and many people think of this as prototypical). But some accidents happen because the road is icy. Some accidents happen because the driver had a seizure and lost control of the car. Some accidents happen because the vengeful spouse of the man you killed in El Paso 15 years ago has finally tracked you down and cut your brake lines. 

Not that we would know anything about that! We’ve never been to El Paso, officer, we swear.

There is no single cause of car accidents. They are more like a symptom. All car accidents look much the same — broken glass, tire marks, people yelling. Most car accidents have similar proximal causes — unless it was an intentional ramming, it happened because someone lost control of their vehicle. But despite these apparent similarities, car accidents can have wildly different original causes. They happened for different reasons.

Consider chronic fatigue syndrome (CFS). Most people assume that CFS is a disease, and that everyone with CFS has it for the same reason, that there is a single cause. But maybe CFS is more like a symptom (obviously “syndrome” is literally in the name). If so, the search for the “cause” of CFS is a mug’s game, since it is caused by many different things. If you go around assuming there is one cause of CFS, one etiology, you are going to end up very confused. 

Or consider irritable bowel syndrome (IBS). Most people seem to be aware that IBS is not really a single diagnosis, and probably is a term used to describe all sorts of different, unrelated things. E.g. “Some people just have trouble with their stomachs. When they have trouble and we don’t know what is causing it, we just call it IBS. So you have IBS.” Even so, the label kind of implies that there is a similarity of some sort, and suggests that maybe there will be some similarity of treatment and of cure. But this may be misleading.

If nothing else, the shared label means that all these people are likely to end up in the same groups or the same communities “for people with IBS”. If someone makes a post like “this treatment cured my IBS”, you can be sure other people will respond with, “well it didn’t cure *my* IBS”. This is guaranteed to be the source of a lot of confusion.

We think that most unsolved chronic illnesses are probably like this — most of them are probably different diseases with different causes that happen to look very similar.

Compare it to the anthropic principle if you like — diseases that present in a consistent way and have a single cause are easy to figure out, so they tend to be cured and don’t tend to be on the list of unsolved chronic illnesses. But diseases where a number of very different causes present very similarly will be quite hard to figure out, and are likely to remain mysterious for a long time. So things that are unsolved and have been unsolved for a while are more likely to have multiple causes. 

(Though even simple illnesses with precise single causes, like scurvy, can be devilishly difficult to figure out, so take this argument with a grain of salt.) 


Single-subject (aka N=1) research can be really powerful. But when it comes to cases like this, you have to be very careful. Even if you do a very rigorous single-subject experiment, and provide strong evidence that some treatment works for you, you’ve only really provided evidence that it works FOR YOU. It may not work for anyone else. 

If the treatment that works for you doesn’t work for most other people with your diagnosis, that’s actually somewhat informative. We can see why some people would find it discouraging, but it suggests that the illness you have “in common” is actually two different illnesses, or at least two substantially different presentations. That means it gets us one step closer, a small step but a step even so, to figuring out what is going on with your illness, and maybe getting a cure or treatment for everyone.

If you end up with Treatment A that works for 20% of people with your condition, and Treatment B that works for 50%, and there’s basically no overlap, you’re off to a great start. You can start looking for anything that the Treatment A people have in common that’s never found in the Treatment B group, and vice-versa. If you find something (“holy cow, everyone who liked Treatment A has Irish heritage!”), you can start directing people to try the treatment that’s most likely to work for them. 

Even if you find nothing in common within the groups, you’re still in good shape. There are only two treatments, and we know that Treatment B works for more people. Newcomers can start by trying B, and if that doesn’t work, they can try A next. If neither work, then they are in the other 30% with no discovered treatment. But it’s still progress in general, and you can start putting your efforts towards finding treatments C, D, E, etc. 

It may be tempting to jump ahead and start looking for differences now, before we have treatments that distinguish between various groups, and there is some merit in this idea. If we find that half of people with IBS tend to have bloating with no reflux, and the other half tend to have reflux with no bloating (or whatever), that’s a pretty interesting sign, and will probably end up being useful. 

But this approach doesn’t usually seem to work.[1] Probably this is because clustering by symptoms isn’t useful; or when it is useful, it will already be obvious. Different causes can present with identical symptoms, as we’ve been discussing. But IDENTICAL causes can also sometimes present with DIFFERENT symptoms! There’s no royal road, no way to cut this knot for sure. You just have to be careful. 

The real enemy here is the confusion (lit. fusion together of different things; “(transitive) To mix thoroughly; to confound; to disorder.”). Talking about “having CFS” or “having IBS” is handy, but when it comes to diagnostics, more detail is better. You may be surprised to discover that someone with the same diagnosis as you has almost nothing else in common. And even when you have every symptom in common, don’t confuse this for a common cause. Your friend may also have migraines, but don’t be shocked when the thing that worked for you doesn’t work for her.

Remember that car crashes all have similar presentation. In true diagnostic fashion, they usually show three or more of the following symptoms: broken glass, injured driver(s), skid marks, bent fenders, police on scene, plastic debris on the road, etc. Take two Geico and call me in the morning. 

it’s ok, this lizard is a doctor

If you only did an analysis of symptoms, you might think that all car crashes have the same cause. An analysis of symptoms would suggest just one group. But we know that’s not the case — car crashes can happen for many different reasons, and even car crashes with very different causes will usually have very similar symptoms. 

Maybe if you are a genius detective and you know just what to look for, you can tell them apart — maybe a car crash caused by a seizure will show signs of uncontrolled driving well before the point of impact, while a car crash caused by excessive speed will have longer, straighter skid marks on the blacktop. But you certainly won’t be able to discover the different causes of car crashes by going down a checklist of “was there broken glass?”, “were there skidmarks?”, “were the drivers injured?”, etc.

If you add in criteria like “how long were the skidmarks?” you might get closer. But you’d have to understand the causes well enough to add that question in the first place.  


[1]: If you know of any examples of looking at a disease, looking for patterns in its symptoms, and finding that it is really two diseases (or something similar), we’d be interested to hear about that, since we can’t think of any examples where this approach has worked.

Half-Tato Diet Community Trial: Sign up Now

In the original potato diet study, we asked people to try to eat nothing but potatoes. This worked pretty well — people lost 10.6 lbs on average over just four weeks.

But we also told them, “perfect adherence isn’t necessary. If you can’t get potatoes, eat something else rather than go hungry, and pick up the potatoes again when you can.” 

People took this to heart. We asked people to track how often they broke the diet, and almost everyone took at least one cheat day.

Five people said they stuck to the diet 100%, but everyone else said they broke the diet at least once. Most people cheated only a few times, but as you can see from this histogram, a substantial minority cheated more than half the time:

Taking these cheat days didn’t seem to matter much. Almost everyone lost weight, even if they cheated a lot:

In general, the more often people cheated, the less weight they lost. But even the people who cheated the most still lost around 5 lbs. 

Realistically, our original potato diet study was really more like a 90% potato diet. People took quite a few cheat days, and it mostly didn’t seem to matter. Makes you wonder how low we can push that percent and still have it work — after all, the original weight loss effect was ginormous.

This is one reason why today we are announcing a 50% potato diet study. We’re looking for people to volunteer to get about 50% of their calories per day from potatoes for at least four weeks, and to share their data so we can do an analysis. You can sign up below.

Case Studies

The other reason we’re doing this study is a number of extremely interesting case studies.

Case Study: Joey No Floors Freshwater

The earliest case study comes from Joey “No Floors” Freshwater, who shared his story on twitter. He did a version of the potato diet consisting of “1-1.5lbs of potatoes a day when I could”. This comes out to about a 20% potato diet, and it turns out the 20% potato diet works quite well, at least for Joey. 

Sadly Joey is no longer on twitter, but we do still have the screenshots:  

Nicky Case Study: Nicky Case

The second case study comes from Nicky Case. Nicky participated in the original potato diet study and lost more than 10 lbs over four weeks, without much difficulty. This is kind of striking because Nicky was pretty lean to begin with.

After the potato diet ended, her weight slowly climbed back up. So 50 days after the end of the potato diet, she started a half-tato diet (“at least ONE meal per day is potato”). On the half-tato diet, she lost weight at about half the rate she did on the potato diet, and described it as “TRIVIALLY EASY to do”. Here’s the figure: 

This is very encouraging. Nicky tried both the potato diet and the half-tato diet for more than 40 days each, and the direct comparison makes it pretty clear that the half-tato diet caused about half as much weight loss, at least for her. 

Case Study: M’s Potatoes-by-Default

Our third case study comes from M, a reader whose email we published in December as a Philosophical Transactions post

M tried a version of the potato diet he calls “potatoes by default”. He describes this approach like so:

If I didn’t have anything better to eat, I’d eat potatoes. This meant that if I had plans for lunch or dinner, I would eat whatever it was I would’ve normally eaten ad libitum, and I tried actively to prevent the diet from materially interfering with my lifestyle (I drank alcohol socially as I normally would’ve, I participated in all the meals I normally would’ve participated in with friends, I tried arbitrary new dishes at restaurants, etc.). … In practice, “potatoes by default” meant I was eating potatoes for roughly 1/3 of my meals, mostly for lunch when I was working from home during the week or on weekends, since I usually had dinner plans of some kind. 

This relatively potato-light approach caused surprisingly rapid weight loss. M describes it like so: “I think my main reaction to the data was that it was kind of insane? I was eating potatoes a third of the time and literally whatever else I wanted the rest of the time, and losing weight almost as quickly as the full potato diet.” 

Here’s the figure. The chart on the right is just a zoomed-in version of the chart on the left, the vertical red line is when he began the potato diet, and the gray bars are when he was traveling and ate no potatoes:

The orange dots in this plot follow the daily averages for the full-tato diet we did. You can see that they are very similar to the blue dots, which are M’s data. When M says that he was losing weight almost as quickly as the full potato diet, he wasn’t joking. While the half-tato diet worked about 50% as well for Nicky, “potatoes by default” seemed to work much better than 50% for M. 

You’ll also notice that M kept on “potatoes by default” for much longer than 30 days, and while the weight loss seems to slow a bit near the end, he keeps losing weight for basically the whole period covered in the plot. He loses more than 10% of his body weight over about three months! And he wasn’t even getting that many calories from potatoes — only like 30%!


That’s why we are running a half-tato diet community trial. Let’s take a look at the design!

Half-Tato Diet Protocol

The half-tato diet is very flexible. As long as you are getting around 50% of your calories each day from potatoes, you’re on target. 

Here are three ways of doing half-tato:

True Half-Tato: Try to get 50% of your calories from potatoes each day, however you want.

Potatoes-by-Default: This is M’s plan, and it worked well for him. Basically, if you don’t have any other plans for a meal, eat only potatoes (a little cooking oil and spices/hot sauce are ok, but nothing substantial). Otherwise, if you are seeing friends or going on a date or anything else, eat as you normally would. If you choose this plan, consider taking a close look at M’s email to us where he describes his protocol in more detail.

Potato Meal: Have one meal a day be nothing but potatoes (with basic spices, etc.). For other meals, eat as normal. This is basically what Nicky Case tried for her half-tato diet. She describes it as “½ the weight-loss effect, but it was *much* easier than Full-Tato. Trivially easy, even.”

On the signup sheet (linked below), we will ask you to indicate which approach you are planning to follow. You don’t have to stick with the approach you choose, but it will be good to know which approaches are most popular, and if there happens to be a big difference between these approaches for some reason, maybe we’ll be able to pick up on it.

When you’re not eating potatoes, please eat as you normally would. The goal is to see how the diet works when you otherwise eat, exercise, and live as normal, so try not to change too much. 

We do, however, have two small suggestions.

In the original potato diet study, we asked people to try to avoid dairy. But now we are not so worried about it. For the half-tato diet, please feel free to continue eating dairy if you want. We will just ask you to track the number of servings of dairy you eat each day on your data sheet. That way, on the off chance that dairy does make a huge difference, we may be able to detect it.  

The second has to do with tomato products, especially ketchup. We reached out to the case studies we mentioned above, and most of them told us that they didn’t have ketchup with their potatoes, or didn’t have it very often, so “no ketchup” may be important for the half-tato diet to work. You may want to avoid tomato products and not have ketchup with your potatoes, but it’s really up to you.

Like with dairy, we will just ask you to track the number of servings of tomato products you eat each day on your data sheet. That way, if tomatoes stop the potato effect for some reason, we may be able to detect it.  

To sum this up:

  • Get around 50% of your calories from potatoes each day, using whatever method (one potato-only meal a day, potatoes-by-default, etc.) you like.
  • Start with whole, raw potatoes when you can, consider cooking them in a way that keeps them as whole as possible.
  • Otherwise, eat as you normally would. Don’t consciously eat better, but also don’t consciously eat worse.
  • On the spreadsheet we share with you (below), track your weight, approximate percent potato for each day, your energy, mood, and the ease of the study, as described on the sheet.
  • Track servings of dairy just in case, don’t bother avoiding it if you don’t want to.
  • Track servings of tomato products, just so we can see if there’s a difference. Maybe consider avoiding them, especially if you’re not losing weight.
  • Track any bonus variables you’re willing/interested to track.

On the first day of half-tato, start eating potatoes as per the approach you chose above (e.g. potatoes-by-default). As long as you are feeling ok, keep trying to stick with it. The effect sometimes takes a couple days to become clear; there’s lots of variation between different people; you may lose a little weight one day and gain weight the next; don’t worry if the effect takes a little while to show up.

If you start feeling bad or weird, try one of these helpful hints:

  • Eating a potato (or something else). Hunger feels different on the potato diet and you may not realize that you are hungry. Yes, really. 
  • Drinking water.
  • Eating a different kind of potato. Different varieties of potatoes may seem like they’re all pretty much the same, but they can really be quite different, and if you’re eating a lot of potatoes, these differences become much easier to notice. You will almost certainly want to eat more than one kind of potato.
  • Peeling your potatoes. Eating less peel / no peel seems to help some people with digestive and energy issues, especially after a few days on the diet.
  • Eating more salt. Potatoes are naturally low in sodium and you may not be getting enough. They’re also high in potassium, which can throw off your electrolyte balance if you don’t get enough sodium to match it. 

If you try these things and still feel bad or weird, take a day or two off the half-tato diet and just mark down on your sheet that 0% of your food (or whatever) for those days was from potatoes. 

If you start feeling really bad, or you otherwise can’t make the half-tato work for you, just stop the trial early. We don’t want anything bad to happen to you. Just send us an email to close out the trial as normal (see below).

Two-Week Baseline

In our previous community trials, we didn’t include a control group. This is because we expected the effect sizes to be ginormous. People don’t, generally speaking, spontaneously drop 10 lbs in four weeks, so it’s clear the weight loss on the potato diet is “real” without the need for a control group.

This worked less well for the potassium trial, but we wanted to get the biggest sample size we could for that study, and we weren’t sure how many signups we would get beforehand. We stand behind the idea that when you’re trying to estimate an effect size, it’s good to get as many people in the experimental condition as possible.

We’re still not going to include a control group, because we don’t think it would be very interesting to recruit half of you to sit around and do nothing for several weeks, and it wouldn’t teach us very much. 

But we will do the next-best thing, and that’s to ask you to take a baseline of your weight change without the half-tato diet. For the first two weeks of the study, eat as you normally would, and track your weight over time. Then on the fifteenth day, start the half-tato protocol and get on to eating lots of potatoes. It’s simple.

This lets us use everyone as a control group for themselves, sort of like a crossover design. While this design wouldn’t work for everything, we think it works pretty well for the half-tato diet. 

Variable-Span Signup

We’d like you to try the half-tato diet for at least four weeks. With the two-week baseline, this is a total commitment of six weeks.

But if you’re willing to go further, we would be really interested to have that data. So for the half-tato diet community trial, we are opening things up and letting people enroll for however long they want.

Credit where credit is due, this part of the design was Nicky Case’s idea. She describes it as a “hey this trial runs for however long you want, and we’ll just report data every month for whoever hasn’t dropped out yet” design, and we think it makes a lot of sense.

This is a bit like what we did with the potassium trial — we asked people to keep going to 60 days if they were willing, some did, and we reported on their data in a second analysis post. We want to do the same thing in this study, except that we’d like to ask you to sign up for longer spans up front, if you’re willing.

We won’t hold you to this. It’s not a commitment. We’d just like to know up front how long you’re planning to sign up for. If you can’t make it that long, that’s fine. Just tell us how long you’re thinking you might try. 

(Obviously you can also keep going for longer if you want, don’t let us stop you.)

For example, you can sign up for:

  • 2-week baseline + 4-week half-tato
  • 2-week baseline + 8-week half-tato
  • 2-week baseline + 12-week half-tato

And so on and so forth, all the way up to 2-week baseline + 68-week half-tato. We will take snapshots of the data at relevant intervals and analyze the data up to that point. 

Sure, “report every month on whoever hasn’t dropped out yet” has a selection bias. The people who sign up for 52 weeks will not be your average ordinary citizens. In fact, they will be paragons, heroes. But that doesn’t concern us. We still want to see those data.

And if you sign up for 52 weeks but it turns out no one can actually be bothered to do half-tato that long, that’s still useful data. Just think about it. 😉 

Sign Up

Ok researchers, time to sign up.

The only prerequisites for signing up are: 

  • You must be 18 or older;
  • In generally good health, and specifically with no kidney problems;
  • Willing to do a two-week period of baseline measurements; 
  • Willing to get about 50% of your calories every day from potatoes, as described above, for at least four weeks, and;
  • Willing to share your data with us.

As usual, you can sign up to lose weight, lower your blood pressure, get more energy, or see one of the other potential effects. But you can also sign up to help advance the state of medical science. This study will tell us something about nutrition, weight loss, and obesity. If the half-tato diet works for most people, it will give us a practical weight-loss intervention that’s much easier than the 100% potato diet.

And beyond that, running a study like this through volunteers on the internet is a small step towards making science faster, smarter, and more democratic. Imagine a future where every time we’re like, “why is no one doing this?”, every time we’re like, “dietary scientists, what the hell?”, we get together and WE do it, and we get an answer. And if we get a half-answer, we iterate on the design and get closer and closer every time. 

That seems like a future worth dreaming of. If you sign up, you get us closer to that future. We hope that this is only the beginning of what will be a century full of community-run scientific trials on the internet. Maybe by 2030, the redditors will have found a way to triple your lifespan. But for now we are doing potato.

Eating this much potato may sound a little daunting, but people who have tried it say that it is much easier than they expected, and delicious to boot. Here’s our suggestion: If you are at all interested in trying the half-tato diet, go ahead and sign up and start collecting your data. Collect your baseline measurements for two weeks, then try the first day or two of half-tato and see how it feels. If you hate it and have to stop, we would still love to have that data.

If at any point you get sick or begin having side-effects, stop the diet immediately. We can still use your data up to that point, and we don’t want anything to happen to you.

We are mostly interested in weight loss effects for people who are overweight (BMI 25+) or obese (BMI 30+), but if you are “normal weight” (BMI 20-25) you can also sign up. The original full-tato diet caused weight loss in people of normal weight, and it would be interesting to see if the same thing happens for the half-tato. 

And for everyone, please consult with your doctor before trying this or any other weight loss regimen. 

If you were part of the original SMTM Potato Diet Community Trial, or the SMTM Low-Dose Potassium Community Trial, please feel free to sign up for this study as well! We know that most people who were part of the Potato Diet Community Trial have returned to their baseline weight in the last 6 months, so the original results shouldn’t interfere. And it will be very interesting to compare your weight loss on the half-tato diet to your weight loss on the full-tato diet. Since we can make direct within-person comparisons, this will give us a much better sense of if the half-tato diet works half as well (or better; or worse) as the full-tato diet.

Anyways, to sign up: 

  1. Fill out this google form, where you give us your basic demographics and contact info. You will assign yourself a subject number, which will keep your data anonymous in the future.
  2. We will clone a version of this google sheet and share the clone with you. This will be your personal spreadsheet for recording your data over the course of the diet.
  3. On the first day, weigh yourself in the morning. If you’re a “morning pooper”, measure yourself “after your first void”; if not, don’t worry about it. We don’t care if you wear pajamas or whatever, just keep it consistent. Note down your weight and the other measures (mood, energy, etc.) on the google sheet.
  4. For the first two weeks, eat as normal and continue to track your weight and other variables to provide the baseline. Then when the two weeks of baseline are complete (clearly marked on the data sheet), start eating about 50% potatoes, and continue with the half-tato diet for however long you signed up for (4 weeks or longer).
  5. We prefer that you try to get around 50% of your calories from potatoes for at least four weeks. But imperfect adherence is ok. If you only get 30% of your calories from potatoes one day, or you have to skip a day entirely, that’s all right. Just note it down on your sheet. We’re interested in how the diet works for normal people at home, with all the complications that entails.
  6. When you reach the end of the diet (whether you’re ending the diet early, reaching the span you signed up for, or going beyond it), send us an email with the subject line “[SUBJECT ID] Half-Tato Diet Complete”. This will give us a sense of how the study is proceeding in general and is your opportunity to tell us all about how the study went for you. Please tell us any information that doesn’t easily fit into the spreadsheet — how you felt, what kind of potatoes you used, how you prepared them, before and after pictures (if you want), advice to other people trying this, etc. There’s a chance that the half-tato approach will work for some people and not for others, and if that happens, we’ll dig into these accounts to see if we can figure out why.
  7. Remember that it is ok to end the study early if you need to, for example if you get sick, or if you decide that 12 weeks or whatever is too long of a commitment. It’s also fine to reach 12 weeks and keep going if you’re having a good time. Just make your intentions clear in the comments on your data sheet and send us an email whenever you decide to finish, we’d love to hear from you.

Assuming we get 20 or so people, we will write up our results and publish them on the blog. We would really like to get a couple hundred people, though, since at that point it becomes possible to do more complex statistical analyses. So if you think this is an interesting idea, please tell your friends!

Interview: Exfatloss on Ex150

This post is an interview with some guy, writing under the name Exfatloss, who has been conducting a weight loss self-experiment and recently put out a blog post about the results so far

Exfatloss has tried a lot of different weight loss techniques, including the potato diet, but nothing seemed to work over the long term. Until now, that is. He has invented a diet he calls “ex150” that has caused a surprising amount of weight loss, and which seems to be quite reliable — at least for him.

This interview is lightly edited for clarity, and to make Exfatloss “sound smart and funny” per his request.

Exfatloss: Hey SMTM, I finally wrote up a summary on my crazy diet experiment, now that I’ve lost just over 43lbs in 5 months. It has a weight graph that I hope you find enlightening.

Feedback from an experimental/author/publication/science/whatever perspective highly appreciated!

SMTM: This is very exciting, and it makes us want to drink some heavy cream right away, yum. Several questions: 


SMTM: For starters let us make sure we understand the ex150 diet as you describe it. It involves:

  • Eating just one meal per day, of:
    • ~150 g meat, usually as
      • ground beef chuck (80% lean / 20% fat) or 
      • ribeye steak
    • ~60 g green vegetables, usually as
      • microwaved frozen vegetables “(okra, spinach, green beans, fajita mix)”
    • ~80 g pasta sauce, usually as
      • “the sauce is low-everything and mostly water (e.g. most store brand tomato/alfredo pasta sauce)”
      • I.e. either red or white sauce
    • As much butter as you want to cook these things in. (“usually about 15g”)
    • None of these things measured or weighed precisely, i.e. the diet seems quite flexible. “I don’t think the exact number matters much.”

Exfatloss: Initially I just cut a 1lb thing of ground beef into thirds. It’s pretty much exactly 150g that way. I’d say it doesn’t matter much if you do 130g or 170g. That’s what I mean by “exact numbers don’t matter much.” If lack of a kitchen scale is holding you back, don’t worry about it, eyeballing it worked fine for me. Now if you were to eyeball double the amount of meat… I dunno. I’d consider that more “ex300” than “inexact numbers.”

tl;dr, just buy 1lb of meat and cut it into thirds.


  • Otherwise eating no meals but:
  • butter and whipped cream, as much as you want, as snacks/desserts
    • Sometimes with instant coffee powder for flavor or tomato sauce to cut the fat taste
    • Quite a lot of it, “I go through a lot of cartons of heavy cream, maybe one every 2-4 days.” How big of a carton? 16 oz?
  • As a result, most calories come from cream.
  • No-calorie foods like coffee are also ok, including coffee with arbitrary amounts of cream, and including going to Starbucks.

Exfatloss: The heavy cream comes in 32oz. I have 3 of those in my fridge right now. I think it’s about one 32oz carton every other day I go through. I put instant coffee powder in the whipped cream most days for flavor.

SMTM: Also you are currently in the USA right? 

Exfatloss: Yes, and have been for this entire weight loss period so far.

SMTM: As we understand the intent behind the design, the butter and whipped cream are there to make it high-fat, the 150 g meat is there to make it a low-but-nonzero protein diet, and the vegetables are there to give some minimum amount of fiber. Does that seem right? 

Exfatloss: Vegetables for flavor/texture and minimal fiber, yes.

Pure ground beef tastes like shit. Trust me, I’ve tried it.

Butter/cream are there to provide calories that are not protein/lithium/whatever the factor is. They’re a known-not-fattening source of calories that also happens to cause no bloating and that I deal with super well. 

SMTM: Butter and cream are “a known-not-fattening source of calories that also happens to cause no bloating”? Our sense is that most people would assume that butter and cream are fattening and might cause bloating, so the fact that you seem so confident is surprising. Known to whom, how? It’s news to us! 

Exfatloss: Well, known to me, at this point 😉 Through trial and error. There are a bunch of people with theories why (low protein, low PUFA, low UFA).. but honestly I have no clue if any of them are right. I just know I lost a bunch of weight eating mostly heavy cream.

I think it’s an important factor of any sustainable diet that you are NOT in a caloric deficit, or it won’t work (Caloric deficit symptoms -> “willpower breakdown” -> quit diet).

SMTM: This also really stands out! It does seem to fit with what we saw on the potato diet. How did you come to this conclusion?  

Exfatloss: Decades of experience? Pretty much any time you restrict your intake or increase your expenditure, you can expect to keep it up for 1-3 weeks or maaaybe if you’re really hardcore a bit longer, and then it stops working and you lose “willpower.” That seems like THE ultimate diet experience of everybody who’s ever tried to lose weight. I write about this in my latest post.

Also when people say “deficit” they are super vague and conflate things and that’s why it’s both necessary and impossible to run a deficit to lose weight. Planning on writing about this at one point.

SMTM: What is the pasta sauce there for? You say, “mostly water”, is that also part of the design? 

Exfatloss: Flavor and to soak up the fat 🙂 It tastes significantly better with the sauce. Maybe that’s just me. This whole meal is my previous go-to meal for over 3 years, just scaled down. I used to eat 1lb of that stuff per day, now it’s ~170g and I added the cream to make up for the calories.

SMTM: It may not matter, but we’re curious, what method do you use to test whether you’re in ketosis? If you tracked your ketones data it might be interesting to graph or publish it as well.

Exfatloss: Currently using a ketone blood meter (finger prick style). I will say a lot of carnivore peeps are calling my “zero fiber != ketosis” statement BS and I’ve updated that section of the blog post to clarify.

Since ketone blood strips are expensive and annoying I haven’t tracked those in years, since first starting keto 7 years ago. So unfortunately no data to show 😦

Would be cool if CGMs could track more than just blood glucose! I would love to have years worth of ketone levels. Good news is that the next-gen Libre Freestyle CGM will have this! Very excited.

Palatability and Variability

SMTM: In your post you talk a bit about hypotheses, including this one:

Palatability/brain hack: there is a lot of science out there around the brain’s ways of dealing with food, food reward, and metabolism. Stephan Guyenet’s The Hungry Brain is maybe the best summary, I think. I’ll admit I haven’t read the book, but I listened to a few podcasts where he talks about the ideas, and I think the ex150 diet fits his hypothesis. The idea is that hyper-palatable food that is very energy-rich causes us to overeat in terms of energy. The ex150 diet has 1 hyper-palatable meal every day, but it is very small. The remaining calories come from a bland mono-food that’s hard to overeat (heavy cream). Maybe this tricks the brain into not overeating the cream, yet never feeling more than 24h away from a hyper-palatable meal to release lots of dopamine or other happy food reward signals? I think that even if this might not be the main causal factor, it sure helps make the diet sustainable. I’m never more than 24h away from the most delicious meal I could imagine, and I can eat unrestricted amounts of “dessert” (=whipped cream w/ instant coffee powder).

This mostly seems like evidence against the palatability hypothesis to us, though it might be interesting to ask Guyenet what he thinks. But to us it seems like you are eating delicious foods and getting a lot of food reward. If as much heavy cream and butter as you want plus “the most delicious meal I could imagine” counts as “low palatability”, then the term is so meaningless that it should be tossed out.

Exfatloss: I do personally think that “palatability” (and “satiety”) are meaningless the way they’re often used, even by Guyenet. I heard him on a podcast where he basically said (paraphrasing) “Science has found that humans tend to be caused by their brain to overeat foods that have high palatability.” Wait, isn’t that the definition of palatability? Very roundabout way of saying “Food that tastes good tastes good.” 😀

SMTM: Yeah that has always seemed kind of circular to us. 

Exfatloss: I’ve @mentioned Guyenet on Twitter, but he didn’t reply (maybe cause I’m a nobody lol). Maybe I’ll ask him again when I have street cred lol. I do think he’s a good representative/explainer of “The Science” on this because he’s got a good grasp of various ideas out there and has been in full-contact debates with Taubes etc. and was able to hold his own. I respect him. I sometimes feel like citizen-scientism is bordering on anti-science and someone knee-deep in science like him is able to check that tendency. That said, in a fight, my money is on Taubes.

SMTM: We like Guyenet and he’s interacted with us a tiny bit, maybe we can help get his attention. We understand that he’s reluctant to engage with weird randos on the internet but we’d be curious to see what he thinks of this.

Exfatloss: Regarding the dichotomy here: I think the meat/vegetables/sauce meal has near infinite palatability, I’ve literally eaten a pound of this before scaling it down for ex150. So if this diet was “eat as many of these tiny meals a day as you want” I’d eat 15 of them. But I can only have 1. The cream/butter on the other hand has extremely low “palatability” in the sense that it’s very hard to overeat.

How do you know you’ve had too much cream? You’ll fricking know. It comes from one second to the next, where the thought of another sip almost makes you gag. Total on-off switch for me, whereas I can literally eat carbs until I puke and not be satiated (ask me how I know. College, man!)

So if “palatability” means something like “able to overeat” then cream is not it, because it’s very self-limiting. Potatoes and dry chicken breast are also very self-limiting, but they’re in fact so limiting that I got into a massive deficit, got caloric deficit symptoms, and had to quit the diet (plus all the fiber made me feel bloated and gross).

SMTM: Your results look a little more like the variability hypothesis, though. We interpret this as a version of (or closely related to) the palatability hypothesis, where the problem is not tasty foods per se, but eating a variety of foods that are tasty in different ways. We think this theory is poorly-supported but ex150 is definitely a low-variety diet, mostly consisting of the same 5 or so foods eaten every day, however delicious they might be.

It’s clear that ex150 is a low-variety diet, close to a mono diet. But it also seems like variety or mono-ness aren’t the active ingredients here because you did other low-variety and/or mono diets and they didn’t work for you at all. If low-variety or mono diets worked for you, then you would have lost weight on the other low-variety/mono diets you tried — on the carnivore diet, on the “eating only at In’n’out burger” diet, and on the potato diet. 

Exfatloss: Yea I do think there’s something to “variety -> overeating.” I do think mono-foods “work” in being self-limiting. My hypothesis here is basically that ex150 manages to hit the goldilocks zone – 1 hyper-palatable meal per day and the rest is a self-limiting mono-food, but it’s not so mono that you get into a massive deficit that makes the diet unsustainable (like potatoes did for me).

On my first week of Potato I was doing only boiled potatoes sans everything, and I couldn’t get down more than 600kcal per day. I’d force myself to go to the fridge, grab a boiled potato, take a bite. It’s not even that the bit tasted bad – but after 1 bite, I was almost gagging. I just couldn’t take a second bite.

So clearly too self-limiting to be sustainable. 200ml of heavy cream has almost 700kcal just on its own and you don’t have to boil it, doesn’t come with all the bloating fiber, digests super easily. Plus you can put instant coffee in it 🙂

SMTM: This isn’t our sense of how the potato diet works in general, since it seems like the tato makes you LESS INTERESTED in other foods.

This seems especially clear in people who have tried half-tato diets. They let themselves eat other foods, but eating a big chunk of potatoes on a regular basis seems to lower your appetite for everything else. For example see M’s experience on the half-tato diet. He says, “maybe two or three weeks in, for the first time in a really long time, I did not have the urge to finish off leftover food at dinner.” Or Joey No Floors Freshwater, who said, “The difference is now I get full and stop eating. I leave food on the plate, which is new for me. I leave texmex on the plate y’all. Its wild.” So the potato diet doesn’t seem to work on just the self-limiting aspects of potatoes because people are less interested in eating other foods too. 

That might be one more reason to do a larger half-tato diet study, to see if this generalizes.

Exfatloss: Hm, that sure wasn’t my experience. For me, it was just an inefficient fast plus the worst bloat in years.

I only did full-tater, maybe half-tater would’ve worked better for me? Not sure why it would work so awesome for some and so badly for me.

One theory I have for that is the goldilocks satiety idea. If you eat a mono food and it’s not satiating enough, you’ll overeat and gain weight. If it’s too satiating, you won’t be able to eat enough to meet energy expenditure and you’ll begin getting caloric deprivation symptoms and will eventually land in caloric bankruptcy (see my post).

Not sure why exactly potatoes don’t hit goldilocks zone for me but do for others. Maybe I have a higher energy expenditure or do worse on potatoes?

Re-reading the section of those people who had potato-success, I do think there’s something to “fix satiety.” Maybe the common thread is that these diets somehow fix satiety in people whose satiety signals are broken.

Of course that just moves the question to “how did the satiety signals break and what fixes them?”

SMTM: To us this looks like evidence against both palatability and variability in your case, and some evidence against them in general, especially if it turns out that ex150 works for other people.

Exfatloss: As those hypotheses are commonly understood, yea. But maybe the Goldilocks Palatability thing? 🤷

I.e. it’s not that “lowest palatability/variety” is optimal, but “modest palatability/variety?”

SMTM: Some other theoretical explanations do come to mind. You’re probably eating close to zero calories from seed oils, so while we don’t find seed oils to be a very plausible theory of obesity, we want to at least note that this result is very consistent with that theory.

Exfatloss: Yea, I’ve actually kind of added that as a new hypothesis. I found this insane s/saturatedfat subreddit after posting my article. People there were like “Duh, of COURSE you would lose tons of fat by eating saturated fat!” There’s this guy Fire in a Bottle (twitter/youtube) who has this whole theory how modern meat (even beef) is full of TCCD (I forget what it stands for but it’s BAD cause CHEMICALS) and PUFAs etc. and that’s what’s making us fat.

I will say I did Paleo for 3+ years before Keto, and Keto for 7 years before this, so it’s not like I was chugging seed oils. But I was eating TONS of US commercial grown beef. So if it’s in that..

It would be cool to design experiments to kind of disentangle the various hypotheses, although I’m not sure exactly how. I suppose a month of 2lbs/day of grass-fed beef? 🤷 Maybe ask the gurus who recommend those theories to design an experiment? They’d know.

SMTM: We also notice that “lots of pure fat” does sound kind of like the Shangri-La Diet, so there might be some connection there. A chemical engineer we work with has repeatedly emphasized that while lithium probably accumulates in many foods, it shouldn’t end up in oils because it’s not fat-soluble. Maybe this is connected to why high-fat diets sometimes work? This isn’t limited to lithium, it’s just generally a note that if obesity is caused by a contaminant, there’s some reason to think that the contaminant doesn’t accumulate in fats.

Exfatloss: Interesting, yea. I remember reading about Shangri La years ago. It does seem very similar. Question is why, I guess: is it “appetite suppression” and what does that even mean – ensuring energy balance is met or some psychological thing?

Have you sent a bunch of meat/milk/cream/potatoes to the lithium lab yet? That would shine some light on it, I imagine.

SMTM: We’re working on it! 😉 

Potato Diet

SMTM: The fact that you didn’t lose any weight on the potato diet and “HATED how bland it was” seems really interesting, especially given that most people on the potato diet said they loved it and many talked about how delicious they found the potatoes, even after 30 days. And for the most part, obese participants had the most success on the potato diet, so it’s interesting that you found it boring.

It kind of suggests there might be at least two kinds of obesity, one that responds to the potato diet and one that responds to ex150. If something like that were the case, it would be pretty easy to demonstrate experimentally.

Exfatloss: I’ve long suspected that obesity is a “slightly complicated problem.” My analogy is a broken down car.

You drive by a car broken down by the side of the road.

You say: “Have you tried putting gas in the tank?”

“Yes, still doesn’t drive.” says the guy

“I’m pretty sure putting gas in cars works, my friend broke down once and he put gas in the tank and then it worked again.”

“Doesn’t work for me,” says the guy in the car.

“I think you’re just not putting enough gas in the tank,” say you.

That’s basically the state of our discourse on obesity, when even much simpler things like cars can break down for a handful of reasons. Maybe spark plugs. Flat tire. Crankshaft. Hell, I’ve had a cylinder blow up on me because the timing belt skipped a beat and one of the cylinders fired out of order.

If there were 4 causes of obesity and 4 different diets to fix them, we would currently conclude that there is no solution and we don’t know and nothing works better than anything else, because we insist on averaging everything out.

On average, putting gas in broken down cars’ tanks doesn’t work. But sometimes it does. When lack of gas is the problem.

SMTM: Yes! We’ve been working on a post about this. People spend a lot of time saying obesity is a disease when clearly it is a symptom that can be caused by all kinds of things. So while there could be just one epidemic, there could equally be several.

There could even be several epidemics for just one reason. If you roll a bunch of cars over a cliff, many of them will break when they hit the bottom and won’t be able to start. But they might all end up broken in different ways, even if the ultimate cause is “was rolled off of a cliff”. 

Exfatloss: Yea “disease” has always seemed wrong to me. I get that people want to take the moral stigma out of it, but “disease” sounds like your immune system will clear it out after 2 weeks or it’s a viral infection or something.

If anything, I’d call it a “condition.”

SMTM: Your experience actually seems like some evidence against the contamination hypothesis and in favor of some kind of deficiency hypothesis. Let’s say that obesity is caused by a deficiency in either X or Y. Potatoes contain X and heavy cream contains Y. If you are X-deficient but have good Y levels, then the potato diet will cure your obesity but cream will taste gross because your body is trying to avoid overloading on Y. If you are Y-deficient but have good X levels, then ex150 will cure your obesity but potatoes will taste gross because your body is trying to avoid overloading on X. 

Exfatloss: I don’t know if I agree, it could easily be consistent with contamination. Maybe potatoes and fats are both very low in contaminants, but some people do super well on fiber and starch whereas others do better on fat. I’m sure people with dairy intolerance will hate my diet, but I used to chug a quart of milk for breakfast as a kid and I can dairy all day. Others apparently love potatoes all day. Personally I do best on “close to zero but not quite zero” fiber.

I think the “feels good on X/Y diet” and “contamination” theories can exist side by side and explain this.

SMTM: Would you be at all interested in running an ex150 community trial, maybe recruiting specifically from people who also found the potato diet bland/difficult? You could start by just getting a couple of other people to try it as case studies, since there’s a rather blurry line between “2-3 case studies” and “community trial of 10-20 people”. If the effect is as strong for other people as it is for you, you wouldn’t need a very big sample size to produce convincing results.

Exfatloss: Yea, definitely. In fact I’m trying to recruit some of my friends 🙂 One insisted on switching to the diet at the same time as doing CrossFit for the first time, because apparently isolating variables is for losers…

If you pointed a couple people my way I’d be happy to set up some kind of study. People can contact me on twitter or the blog, or can email me at hello@exfatloss.com

SMTM: Absolutely! People will see it in the blog post and we’ll share about it on twitter, we’ll encourage people to email you there.

Exfatloss: Sounds good!

If it’s just a handful of people I think I’d be more comfortable managing it. I’m not sure I’d be up for hundreds of people like you had on Potato because I’ve never done that type of thing before. That seems to require infrastructure.

SMTM: Yes, and it’s good to start with more case studies before scaling it up to dozens of people. Better to make sure it generalizes and we can re-create it so we don’t waste everyone’s time. 

Exfatloss: Yea for sure. Maybe I’m just somehow a crazy sat fat outlier who can’t deal with potatoes 🙂

Boundary Conditions

SMTM: You mention,

For example, could you make the diet work eating only at common fast food restaurants? Using only prepared deli meats? What about cheese? Is it really just about the amount of protein, or does it matter what kind of protein? Eggs?

Does the diet even need to be ketogenic? What happens when you reach a healthy weight, can you back off the diet? Do you cycle it? Is there a maintenance version?

These seem like the most important questions to us. If you switched to 150g eggs + different vegetables + as much olive oil as you want, would it persist? What about other formulations?

What if you stick with the original formulation but slowly add rice until you are no longer in ketosis? You wouldn’t necessarily need to go out of ketosis if you could show a correlation between the rate of weight loss and your ketone levels (though obviously dropping out of ketosis without it affecting the weight loss would be most convincing). 

Exfatloss: I’m actually currently on day 5 of ex150deli, which substitutes supermarket deli meat cuts (salami, turkey breast, roast beef..) for the ground beef. I kept the vegetables/sauce the same for science’s sake and let me tell you, they do NOT go well together with sliced deli meats lol. [SMTM note: since finished with success, see here]

Agreed that this would be awesome. It seems there’s gotta be a whole lot of alpha out there in fat loss, and we’re probably nowhere near the efficient frontier. So we should explore the boundaries. What do we actually have to give up to be successful? Why give up more.

SMTM: We also noticed that you say,

What piqued my interest though was that the super-low-protein carnivore diet, while it still kicked me out of ketosis, made me rapidly lose weight, about 10lbs in the 12 days until I ended the experiment early (because I was out of ketosis already, proving the hypothesis).

This story suggests that such a thing is possible. 

Exfatloss: See my 2/17/2023 update on the post on the fiber/ketosis issue, several carnivore people claim I’m wrong on this and I concede it’s possible.

SMTM: It also seems like this could just be a cream-maximalist diet, right? Do you know about how many calories you’re getting per day, and how many of them are from cream? Seems like it’s over 50% right? 

Exfatloss: 50%? Ha. It’s 85% before I add the cream in the coffee 🙂 I have a macro estimation in this blog post.

SMTM: There would be a darkly comic element if obesity was cured by high doses of cream, but it would also make some sense. What is the one treatment for obesity that no one would ever think to try? “Drink as much heavy cream as you can stand, every day.” We’re confident that most people would never try this (ok maybe some people would on keto), so it would make sense if everyone missed it… 

Exfatloss: Ha you should see the r/saturatedfat people.. as I understand it, the claim is literally that eating saturated fat will increase your metabolic rate by insane levels and thus create a massive bottom-up deficit.

SMTM: In general, is there a principle of “if you’ve been looking for a long time and tried everything you can think of and nothing works, the real answer must be something that seems really stupid”? Reminds us of Sherlock Holmes’ “when you have eliminated the impossible, whatever remains, however improbable, must be the truth.”

Exfatloss: I will confess to having read a lot of Sherlock Holmes.

SMTM: Us too! 

Superstition and ABA

SMTM: Generally we are concerned about the “superstition” element of self-experiments. If spontaneous remission is a possibility, and you try a long enough list of things, you might randomly spontaneously remiss and it would look like the thing you were trying at the time is the cause: 

Let’s say that Mary develops chronic fatigue syndrome (CFS). She is proactive and wants to solve the problem, so she comes up with a plan of 26 different treatments, which we’ll call A, B, C, D, and so on. Maybe A is “cut out dairy”, B is “walk 20 minutes every day”, etc. but the specific plans don’t really matter. She starts implementing each plan for two weeks, first plan A, then plan B, etc. 

But Mary is working from the wrong assumption. She thinks her chronic fatigue comes from something she’s doing or not doing. … But what really happened is that last month she bought a bag of rice that was grown in a field that was contaminated with cadmium, and developed low-level cadmium poisoning, which is entirely responsible for her chronic fatigue. …None of the interventions she has planned will help. 

But the cadmium is slowly being cleared from her system by natural means at the same time as she works her way through the 26 treatments. What happens is this: Mary reaches treatment L (“take omega-3 supplements”) just as the cadmium in her system drops below critical levels, and Mary is immediately “cured”. 

Since her symptoms stop almost immediately after starting treatment L, Mary assumes that the omega-3 supplements are what cured her, and continues taking them indefinitely.

This is basically what happened when you moved back to the US from China. 

So we REALLY like how you took a 14-day break from ex150 right in the middle of your self-experiment. If you were randomly losing weight for some other reason, then you should have kept on losing weight during this break. The fact that you gained weight back, and that it closely corresponds to the break (modulo pemmican), seems like strong evidence that, as you say, “it wasn’t some other random factor in the environment causing the fat loss.” 

We see the same thing in a smaller way in two other short breaks you take.

This looks a lot like an ABA design, or since you have four experimental periods, an ABABABAB design. 

Usually we would say that ABA-type designs don’t really provide enough evidence to draw clear conclusions. Even with an ABABABAB, that’s still only a sample size of 8 intervals. But in this case, the effect seems so distinct and so the effect size so huge we’re not sure. What do you think? 

Exfatloss: Definitely agreed that it doesn’t prove “what did it” or even anything.

But it disproved a bunch of really likely environmental factors like a) city (walkability? air quality?) b) weather/temperature c) drinking water d) cancer haha.

I think it’s a really easy and pretty good thing to do. If you really know why the light turns on and off, you shouldn’t be afraid to hit the switch a couple of times and see if it works as you thought. That’s kind of the least you can do. If you never turn the light off because you’re afraid it won’t come on again, does that really sound like you understand why it’s on in the first place?

Set Point 

SMTM: In your Q&A section you give this exchange: 

Q: You’re just going for walks now.

A: No, fat loss started 2 months before that and the rate hasn’t changed. But yes, I feel so energetic many days on this diet that I started spontaneously wanting to go outside and take long walks. One time I even fell into a light jog! In my experience this is a result of effective fat loss, having “unlocked” the key to utilizing my body fat, not the cause of fat loss. 1,150kcal/day (0.3lb of body fat) would be a long walk to take every day.

This is interesting to us because it suggests your set point is falling faster than your weight is. Compare this experience to how you mentioned that running as exercise just makes you hungrier to compensate for the extra calories you burn. So that suggests that something about this diet changes your set point very quickly, which seems interesting. 

Exfatloss: I kind of believe that we’re thinking about “set points” slightly wrong. This is inspired by my understanding of circadian rhythms.

All humans have a “genetically predetermined circadian rhythm.” But it’s not that somewhere in your genes it says “8am EST” or anything. The best analogy I’ve read is that what’s basically encoded is a spring weight. Imagine your circadian clock is powered by a spring, and sunshine pushes down on the spring. Different people have different spring weights. Most people’s weight is such that if they get even a little bit of sunlight during a normal day, their spring is fully compressed and ready to go again. Some people have a very stiff spring, and they need enormous amounts of sun exposure to get it compressed during one day.

If you move even normal people to the north pole or something, even their springs will never compress (in the constant dark) or always be overcompressed (in the constant sunlight). If you put people in a cave, the spring mechanism just completely stops working.

My point being, what if it’s not that we have a “set point” that says “He shall be 210lbs” but instead, the rate of how “calories in” is split up? Similar to the P ratio. This ratio could be influenced by various factors like macro composition, chemicals in the food, sunlight, sleep quality.. some people have a ratio in such a way that pretty much no matter what they do, the calories they eat will be sent to the furnace. Other people will have ratios that require them to take super extreme measures to prevent gaining fat. If you put healthy people on a PUFA-sugar-juice diet and sleep deprive them and feed them tons of lithium, even they will probably gain fat.

For example, maybe I’m just an insulin hyper-responder, and what normal people consider “normal” amounts of carbs or protein makes me obese. And suddenly my ratio has swung from one end of the scale (->90% of calories in go to fat) to the other (->90% calories are sent to the furnace and you will fricking go for a walk every day even in freezing rain just cause you can’t stand sitting still).

Maybe it’s not that this ratio per se is encoded. Point is it could easily be encoded as a flow rate, not as an absolute “set point == 210lbs” value. And you just reach a different equilibrium with your current environment depending on the flow rate/spring rate. Just as you’ll reach a certain “waking set point” in the winter, and a different one in the summer, depending on factors like sun exposure.

SMTM: This is a good argument, but the difference between circadian rhythm and metabolic set point is that while the body doesn’t have access to a direct measure of time (it uses external cues like sunlight), it does have access to internal metrics about obesity. This seems to involve signals like leptin, literal compressive weight on your bones, blood sugar, stomach fullness, etc. 

Exfatloss: But those metrics aren’t an objective, comprehensive obesity score like body fat %. It’s different chemical signals. Those signaling pathways can be disrupted or conflated or confused.

In a computer analogy, there isn’t one program in your body that can read the total fat storage value and set the heater/AC accordingly. It’s a bunch of distributed systems sending each other messages in various ways. If something goes wrong with some of the packages, unspecified behavior can set in. The TCP port could be blocked. The pipe could be broken. Your packages might get misrouted by a rogue/broken system in the middle. There might be backpressure in the signaling system that changes the frequency/density of the packages arriving.

Might also be personal. For example, my sensitivity to physical stomach fullness is practically zero. I always assumed that people meant this figuratively. I have literally eaten until I was painfully full and felt zero satiation. I wanted to continue, I just couldn’t, from the pain.

Pro-tip: never go to an all-you-can eat pizza place.

The potato diet wasn’t quite that bad, but it was also really bad.

On the other hand, the whipped cream satiety hits me like a cement truck. One bite fine, second bite good, third bite NO WAY I’M FULL. (These are the last 3 bites, not the first 3 bites, of a whipped heavy cream meal.)

SMTM: How about the hairpin turns when you try going off the diet and back on again? Whatever this diet is doing, your weight seems really responsive! That’s weird, but it kind of matches the results on the potato diet, which also seems to cause abrupt changes in most people’s weight. 

Exfatloss: A lot of the hairpin is water retention. I’ve seen as much as +6lbs the day (!) after ending my second ex150 month, and -4lbs after the first day of pemmican.

One confounder with potato for me is actually that I was pretty low-fiber before it, because I hate fiber. So I went from a low-fiber to an all-fiber diet, which would jack up my water retention. So given the above numbers, even if I lost 6lbs on potato, the very first day of increased water retention could negate it.

Btw these water retention effects are plateau effects, which was my criticism of your recent potassium study. But if you do switchbacks, it creates these insane hairpin turns.

I basically disregard the first, really steep weight loss when I go on the diet. Usually it takes at most 5 days to finish the plateau effect and for the “real” fat loss rate to show.


SMTM: We agree that the real point is the meta-framework of experiments, so we’re really interested to hear more about these other things you tried that you list near the beginning of your post (cold showers, no online news, carnivore diet), what can you tell us about those? 

To emphasize: the real point is the meta-framework of experiments. Formulate a hypothesis, design a 30-day experiment, test it. I’ve probably done dozens of these over the years.

Here are some examples from the last few years:

30 days of cold showers

90 days of no online news (I thought stress might contribute)

90 days of the carnivore diet

30 days of eating only at In’n’out burger

Doing Starting Strength, a beginner’s powerlifting program

Doing Simple & Sinister, a kettlebell training program

30 days of a low-fiber diet

30 days of a low-protein diet

30 days of a potato diet

30 days of drinking only distilled water (including for coffee)

Eating only pemmican, a raw meat paste invented by Native Americans

Exfatloss: Ha I’m planning to eventually write a longer post where I detail some of these experiences. [SMTM note: this post has since been written, see here]

Some highlights: cold showers did nothing. No online news (suggested by a friend) showed me that I consume news as entertainment, but that I just replace it with movies/video games when I stop consuming the news. Carnivore diet was super bland and boring (YES steak gets boring!) and I didn’t lose any weight. In’n’out was the best, I love that place! My first low-protein trial was entirely done at In’n’out, as was the low-fiber one. Starting Strength made me fatter. Pemmican was even more unpalatable than potatoes lol, I chewed every bite for 2 minutes. It just tastes like I imagine cow manure tastes like. Ugh. Sad because I really wanted to like it.

SMTM: A lot of science criticism seems really facile. In particular, it seems like lots of people don’t understand measurement, they think that measuring things is both objective and easy. It makes us wonder if these people have just never tried to measure anything for themselves so they don’t realize what is involved (compare: Reality has a surprising amount of detail). So our sense is that trying a lot of failed diets is part of what has made you a careful experimenter. What was your experience of this? Does this have practical implications for training, or for people who want to get into research / self-experimentation? You seem very virtuous to us. What advice would you give to other people who wanted to do self-experiments like this?

Exfatloss: Hm, not sure. I just like experimenting and trying new stuff, I’d probably keep doing it even if I reach my goal weight. Just for fun.

Most experiments have no effect or almost no effect. “It makes no difference” seems the default result.

SMTM: Good insight.

Exfatloss: All diet experiments that somehow rely on you eating less or burning more energy seem to fail very quickly because caloric deficit symptoms set in. I call these diets “inefficient fasts” because you could’ve saved yourself 15 days and gotten too hungry to continue by water fasting, instead of getting too hungry to continue on day 18 of your diet.

Even when you’re really, absolutely, positively sure you identified The Thing, you can be completely wrong. This was my experience after “knowing” through experiment that keto is what made me lose 100lbs. I had literally already written the book 🙂

Lesson in humility. One of the reasons why I’m couching my terms more this time and mostly going off of my experience so far. One of my broader claims this time was the zero-fiber/ketosis thing, and apparently I’ve already been proven wrong. Zing!

I am very critical of Science(tm) as an institution, especially in fat loss and nutrition. It seems that a lot of scientists hide behind mouse models and sophisticated studies so they don’t have to face the fact that nobody actually fricking knows how to lose weight.

Saw a meta-analysis recently that concluded “all diets work well to reduce weight.” Really? Must’ve not heard about this obesity epidemic.

That’s why I love the citizen scientist stuff so much. I think modern ethics boards literally make it illegal to do meaningful diet research.

SMTM: Preach!

Exfatloss: Try getting a 85% calories from heavy cream study approved.

Final Thoughts

SMTM: Finally, can we publish your responses to these questions (and responses to any followup questions) as an interview on our blog? If that sounds good, we’ll produce a version of this email thread, edited for clarity and flow, and go over it with you before publishing. 

Exfatloss: Yea, that sounds great! Please edit it to make me sound smart and funny lol. Exfatloss is good as a name. You can mention that I’m a guy just for clarity, as that can make a big difference in fat loss/metabolism I think.

SMTM: How should our readers reach you if they have questions? Comments on your Substack? Email? 

Exfatloss: Substack is best. Also on Twitter @exfatloss


And thank you for inspiring me to try this shit again, I had given up until I read the Lithium series.

4 jeans sizes is already so worth it. You wouldn’t believe the quality of life difference 40lbs makes. Literally wouldn’t believe it. If I never lost another pound, this would still be a huge success.

SMTM: ❤ ! 


N=1: Why the Gender Gap in Chronic Illness? 

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition


Many chronic illnesses are much more common in women than in men. IBS is about 2-2.5 times more common in women than in men; migraines are about 2-3 times more common; chronic fatigue is about 4 times more common. 

This is pretty weird, and more than a little mysterious. And it’s doubly weird that the ratio is pretty similar — each of these examples is about 3 times more common in women than in men.

Normally this gender gap, if it is addressed at all, is written off as a biochemical difference (e.g. here). But another possibility is that gender is just a proxy for body size (e.g. here). If some chronic illnesses are caused by exposure to irritants, heavy metals, or other contaminants, smaller people will generally have more of a response to the same level of exposure, and women on average are smaller than men.

If this is the case, it should be possible to detect if gender is a proxy for body size in some chronic illnesses. If body size is what really matters and gender is just a proxy, larger-than-average women will be underrepresented and smaller-than-average men will be overrepresented. Basically, once you know someone’s height and weight (and maybe % body fat), their gender shouldn’t give you any further information about their likelihood of getting sick.


We can show this with some simulations.

Here’s a simulation of 10,000 men and 10,000 women. The men have an average height of 69 inches with a standard deviation of 3 inches, and the women have an average height of 64 inches with a standard deviation of 3 inches. 

Let’s start by seeing what things look like if the greater prevalence of women is the result of something like hormone levels, and body size has nothing to do with it. In this case, the men all have a 1% chance of getting the illness, and the women have a 3% chance. Height doesn’t factor in at all. So when you look at the distribution of heights of men and women in the group of people with the chronic illness, it looks something like this:

As you can see, three times as many women have the illness as men do, but otherwise the distributions are quite generic. These are basically just subsets of the distributions for each gender. They should be normally distributed and should generally look similar to one another, except that there are more women than men and the two groups have different average heights.

Now in comparison, we can consider what the data would look like if gender is just acting as a proxy for height, and there are more women with chronic illness only because they are shorter on average. 

Here’s another simulation of 10,000 men and 10,000 women, with the same distributions for height. Without getting into the exact model,[1] this is what it looks like if height is the only thing that determines if you get sick, and shorter people are much more likely to get sick: 

Again we see that there are about three times more women than men, even though this time, gender doesn’t have a direct effect. In this simulation, height is the only thing influencing who gets the illness, but the difference in average height is enough to make it so that there are three times as many women as men. 

While it’s not clear from just eyeballing the distributions, there are signs in the data that height is driving this difference. For example, about 1% of women are 70 inches or taller in the height-based simulation (compared to about 2.2% in general) and about 9% of men are 63 inches or shorter (compared to about 2.2% in general). This seems like a clear sign that height is the actual thing that determines who gets sick.


Since we don’t know what the real-world dynamics would look like, it’s not clear what you would see in real-world data. It could just be that people with the chronic illness would be shorter on average than people without — American women are about 64 inches tall on average, so it would be interesting if the average height on a chronic illness subreddit was just 61 inches (though you might want to account for age and ethnicity). If the effect was strong or nonlinear enough, there might be a noticeable skew in the data instead. Or you might see the underrepresentation of larger-than-average women and overrepresentation of smaller-than-average men that we describe above.

You could conceivably detect this kind of difference with normal survey methods, as long as you got a large enough sample size. To our mind, evidence that height (or possibly weight, you would want to collect both) explains why women are much more likely to have a chronic illness would be evidence that the chronic illness in question is caused by some kind of contaminant, since other causes shouldn’t be so sensitive to body size. If anyone wants to help collect this data for their community, please contact us.

[1]: The probability of a simulated person getting sick was proportional to 82 inches minus their height in inches, cubed. That is to say, in this model someone who is 56 inches tall was 17,576 times more likely to get sick than someone who is 81 inches tall. These numbers mean nothing, we pulled them out of our ass.

N=1: Hidden Variables and Superstition

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research


Our psychology is focused on behavior. We focus on behavior because we want to figure out what actions we can take to influence the world around us. But a focus on our actions can also make us superstitious. 

The classic example is from a BF Skinner study, where he put a bunch of pigeons in a box and dropped in food at random intervals. Instead of realizing that the food drops were random, the pigeons assumed that they were somehow responsible and tried to figure out what they had done to make the food appear. 

Whatever they were doing at the time the food dropped, they tried again. A pigeon who had just turned counterclockwise when the food arrived would turn counterclockwise again and again. When more food eventually did arrive, the counterclockwise-turning was validated. “The experiment might be said to demonstrate a sort of superstition,” wrote Skinner. “The bird behaves as if there were a causal relation between its behavior and the presentation of food, although such a relation is lacking.” [1]

Compare this to a rat confronted with a set of buttons, trying to figure out which of the buttons give food and which give painful electric shocks. Unlike the pigeons, the rat is faced with a deterministic system where her actions lead directly to reward and punishment, so her focus on behavior is justified and leads to a correct understanding of the system. The pigeon is faced with a random system where his actions have nothing to do with the arrival of food, so his focus on behavior is pointless and leads only to superstition and confusion.

so cute though!


We worry this is a common problem in chronic illness. Let’s say that Mary develops chronic fatigue syndrome (CFS). She is proactive and wants to solve the problem, so she comes up with a plan of 26 different treatments, which we’ll call A, B, C, D, and so on. Maybe A is “cut out dairy”, B is “walk 20 minutes every day”, etc. but the specific plans don’t really matter. She starts implementing each plan for two weeks, first plan A, then plan B, etc. 

But Mary is working from the wrong assumption. She thinks her chronic fatigue comes from something she’s doing or not doing. In short, she thinks it comes from her behavior. This is a common assumption because our psychology is focused on behavior — we look for things we are doing right or doing wrong. But what really happened is that last month she bought a bag of rice that was grown in a field that was contaminated with cadmium, and developed low-level cadmium poisoning, which is entirely responsible for her chronic fatigue. Cutting out dairy or walking to the corner store won’t do a thing, because the cadmium is the only cause of her illness. None of the interventions she has planned will help. 

But the cadmium is slowly being cleared from her system by natural means at the same time as she works her way through the 26 treatments. What happens is this: Mary reaches treatment L (“take omega-3 supplements”) just as the cadmium in her system drops below critical levels, and Mary is immediately “cured”. 

Since her symptoms stop almost immediately after starting treatment L, Mary assumes that the omega-3 supplements are what cured her, and continues taking them indefinitely. In reality, the omega-3 supplements do nothing for her — as long as her cadmium levels are low, she doesn’t have CFS, and if she ever gets exposed to high enough levels of cadmium again, her chronic fatigue will come right back.


What Mary should do is she should run a self-experiment with the omega-3 supplements. She should randomly assign some weeks to be on omega-3 supplementation, and some weeks to be off. If she did this, she would quickly find that the omega-3 makes no difference to her chronic fatigue.

It’s understandable why she doesn’t try this — she is worried that if she stops taking the omega-3, her chronic fatigue will come back, and she doesn’t want to risk it. Also, we suspect she wants a world that makes mechanical sense (“I just needed to take more omega-3”) rather than a world where she randomly gets sick and there’s nothing she can do to stop it. It’s hard to blame her for that.

This is how the focus falls on behavior and misses hidden variables. By “behavior”, we mean actions that are directly under people’s control. Eating more or less of something, getting up earlier or later, trying more or different kinds of exercise, and so on. By “hidden variables” we mean essentially any variable you wouldn’t normally think of, especially one not connected to your actions. For example, heavy metals in your drinking water, additives in your food, viruses you contracted from your friends, air pollution from forest fires hundreds of miles away, mold in your ceiling, or things you’re exposed to at work. 

Most of these hidden variables can be influenced by your actions, but they’re not the kinds of behaviors that come to mind. You can always quit your job, but for most people, that doesn’t come to mind as a possible treatment for their illness. You can cut out spinach or dairy, because “eat less dairy” is psychologically simple — but “consume less sulfites” isn’t a clear action for most people because “foods with sulfites” isn’t a category to most people. They may not always know which foods contain sulfites, and they may not know what sulfites are. 

Does this look like the face of mercy?


This is what chronic illness looks like for Mary as an individual. At the group level, things look somewhat different. 

If a chronic disease is caused by a hidden variable (like cadmium randomly being in some foods but not others), you should see something like this: People get sick for apparently no reason. They all try many different treatments, and most treatments don’t seem to work for anyone. Sometimes a treatment will seem to work for a bit, but then it will unexpectedly stop working. Whenever you feel like you start to get a firm grip on things, all the rules you learn go out the window. However, there are many individual stories of trying some new treatment and suddenly being cured. Unfortunately, the cures in all of these stories are entirely different treatments, and the cures that work for one person never seem to work for anyone else.

And this does sound like what we see in many chronic illnesses, which makes us suspect that some chronic illnesses are being caused by a hidden variable. It could be contamination in food, water, or air, like our hypothetical Mary’s experience with cadmium. But it could also be any other unexpected variable that doesn’t have to do with personal behavior. For example, it could be the result of a virus, or an allergy to something in your household, or a curse put on you by the local witch. When taken as a group, chronic illness communities look exactly how we would expect them to look if the illnesses were caused by some hidden variable, and that makes us suspect that they are caused by some hidden variable.

Naturally this makes us wonder if there is any way to figure out what these hidden variables might be, assuming you believe they exist. The fact that they are hidden does make it inherently tricky, but we have a couple of ideas, here they are.


Your chronic illness may be triggered by something in your environment (your home, work, local food, local water, etc.). To test this, you can change as much of your environment as possible all at the same time, for example by taking an extended trip to Nepal. 

If you start feeling better or your symptoms disappear, this strongly suggests that something in your home environment is causing your illness. If you don’t feel any better, it suggests that your symptoms 1) aren’t caused by your environment, 2) are caused by elements of your environment that you brought with you (e.g. your clothes, your shampoo), or 3) are caused by elements of your environment that are common to both your home and Nepal (car exhaust?).

Your chronic illness might also be triggered by something you eat. To test this, you can change as much of your diet as possible all at the same time, for example by trying the potato diet, where you eat essentially nothing but potatoes. The potato diet is good because potatoes are simple, contain no additives, and are more or less nutritionally complete. Many people can survive happily on nothing but potatoes, salt, water, and hot sauce for up to four weeks (we have good data on this!). 

If your symptoms disappear or get better, this strongly suggests that either you had some deficiency that the potato diet fixed, or something in your normal diet is causing your illness. If your illness is just as bad as ever, it suggests that either your symptoms aren’t caused by your diet, or are caused by elements of your diet that are also in the potato diet.

Neither of these approaches will tell you what is causing your illness, but both have the potential to narrow things down enormously. If you go to Finland for a month and your migraines stop three days in and don’t come back until you get home, that’s pretty clear evidence that something at home is causing your migraines. You don’t know if it’s your laundry detergent, your well water, or something at your job, but you can take steps to narrow it down further, and you can stop worrying about your diet so much. 

Similarly, if you try the potato diet for a month and your executive function issues disappear, you can stop worrying about fumes from your boiler and can try to figure out what part of your diet is giving you brainfog.

Even a null result is informative. If you go on the potato diet for a month and your migraines carry on as normal, that’s a pretty clear sign that it’s not something in your diet, and you should look elsewhere. 

Some people find this approach surprising, because scientific investigation usually involves isolating a small number of variables and putting them under tight control. This works fine when you have a small number of variables to start with, or you know which variables you’re interested in. But in the search for hidden variables, there are a nearly infinite number of things that could be the cause of your illness. We need a technique that lets us rule out lots of theories at once, so doing these big splits can be extremely productive.

There’s a classic genre of logic puzzles often called balance puzzles. In these puzzles you have several coins, one of which is lighter than all the others, and you have to use a balance scale to find the light coin in the smallest number of weighings. The way you solve these problems is by splitting the coins into groups and comparing the groups directly. If you split the coins into two groups and the group on the right weighs less than the group on the left, the light coin must be in that group.


Consider a version of this puzzle where there is an illuminated lightbulb and a row of 1,000 switches. You want to find the switch that controls the lightbulb, but you don’t know which it is. You could go down the row of switches and try them one by one, but this would probably take you several hundred steps. On the other hand, if you have some kind of opportunity to flip a bunch of switches at once, that can narrow things down really quickly. 

Let’s say that half the switches are red and half are blue, and you can flip all the switches of a single color at once. If you flip all the red switches and the light goes off, then the master switch must be red. If you flip all the red switches and the light stays on, then the master switch must be blue. Either way, you now have only 500 switches to try. 

This is the same situation we’re in with chronic illness, except that there are something like 1,000,000 switches on the wall, and in some cases the lightbulb might be controlled by complicated interactions between multiple switches. It still makes sense to toggle big groups of switches all at once when you can, because that can narrow things down drastically.

One limitation of this approach is that it’s only really good at finding triggers. If you’re suffering from an iron deficiency, big eliminations probably won’t help find that.

The other limitation of this approach is that it’s not always clear how long you have to eliminate things for. Do you need to eliminate the mystery trigger for a week? A month? Longer? Ideally we could send you to Nepal for a year, or put you on the potato diet for a year, but in reality this won’t be practical for most people.

If Mary is getting poisoned by cadmium, and it takes two months to clear all the cadmium from her system, then going on a restrictive diet for only one month won’t help. But the problem is, she can’t know this in advance. How is she supposed to know about the clearance rate when she doesn’t even know what’s poisoning her?

So we’re stuck with an asymmetry. If one of these eliminations helps you, that narrows things down quite a bit. But if they don’t help you, then it’s more ambiguous. Maybe the half-life of whatever is making you sick is just too long. Still, it seems like it would be worth trying.


Another possibility is to just try various things and see what works. This is grasping in the dark, but we can still do a lot to cover our bases.

For example, there are a finite number of vitamins. “Vitamin deficiency” is a plausible type of hidden variable, so you could just cycle through all the vitamins and see if any of them happen to be an immediate cure. It seems unlikely that you will get this kind of miracle result, but vitamins are pretty safe so the risk is very low, and you could at least check “vitamin deficiency” off your list.

Doesn’t happen very often, but you know, sometimes it does

Similarly, there are a finite number of elements. Some of them, like iron and potassium, are necessary for human health. You could try supplementing these and see if that treats your illness. Other elements, like lead and mercury, are known to be bad for your health. You could try getting blood and/or urine tests, or testing your local water supply, and see if you have higher exposure to any of these known toxins. 

Again these are all shots in the dark, but they’re all plausible variables that could be affecting your health. If it turns out your blood mercury levels are way higher than normal, that would be good to know.

You could also try to hit your illness with some generic treatments, basically anything where the name starts with the prefix “anti-”. If you can convince your doctor, you could maybe get them to put you on a broad-spectrum antibiotic (in case your chronic illness is bacterial), antiviral (in case your chronic illness is… etc.), antifungal, anti-inflammatory, or antihistamine. This is a little more risky, but there’s some chance your chronic illness might be fungal and this is one of the only ways you would ever find out. 

This broad-spectrum approach will generally be better for finding deficiencies, but in some cases it might also help identify triggers.


Haha, but no, seriously. Donating blood is easy, safe, and it’s a nice thing to do for your community. You might save a life. And if there’s something nasty building up in your blood, you might be able to get rid of some of it. There’s already some evidence that donating blood can reduce your serum PFAS levels. Maybe it can clear some other things from your system.

Again, this is a pretty blind approach. It probably won’t work for most people. It may not work for anyone at all. But if you donate blood and your symptoms immediately get better, that would be pretty interesting, right? 


In the beginning, we’ll be taking shots in the dark. People will try dozens of things with little or no success. This will be quite frustrating.

But the hope is that eventually, we will start to get our bearings. If a couple people with chronic fatigue find that they have high levels of cadmium in their blood, then other people with chronic fatigue will want to check their cadmium levels before trying other interventions. Conversely, if a couple dozen people with chronic fatigue check for cadmium and find nothing, checking for cadmium should be moved lower down on the list for chronic fatigue. 

Over time, some people will get very skilled at organizing and interpreting this kind of research. And with enough people trying things and going over the data together, tricky bugs quickly become shallow

Depending on the success of this approach, you can even imagine this being somewhat formalized. Someone could make a centralized list of things to try or to have tested, and people could report what they had tried and whether it worked out. Tests that seem to be helpful could be moved up in the rankings so people could know to try them first; tests that don’t seem to help people could be moved down and left for the last ditch attempt.

Will this work at all? Who knows, but it seems like it’s worth trying. And at the very least, we may be able to rule out some hypotheses. After all, if we can establish that it’s none of the things on this list… then what the hell is it? 



“A pigeon is brought to a stable state of hunger by reducing it to 75 percent of its weight when well fed. It is put into an experimental cage for a few minutes each day. A food hopper attached to the cage may be swung into place so that the pigeon can eat from it. A solenoid and a timing relay hold the hopper in place for five sec. at each reinforcement.

If a clock is now arranged to present the food hopper at regular intervals with no reference whatsoever to the bird’s behavior, operant conditioning usually takes place. In six out of eight cases the resulting responses were so clearly defined that two observers could agree perfectly in counting instances. One bird was conditioned to turn counter-clockwise about the cage, making two or three turns between reinforcements. Another repeatedly thrust its head into one of the upper corners of the cage. A third developed a ‘tossing’ response, as if placing its head beneath an invisible bar and lifting it repeatedly. Two birds developed a pendulum motion of the head and body, in which the head was extended forward and swung from right to left with a sharp movement followed by a somewhat slower return. The body generally followed the movement and a few steps might be taken when it was extensive. Another bird was conditioned to make incomplete pecking or brushing movements directed toward but not touching the floor. None of these responses appeared in any noticeable strength during adaptation to the cage or until the food hopper was periodically presented. In the remaining two cases, conditioned responses were not clearly marked.

The conditioning process is usually obvious. The bird happens to be executing some response as the hopper appears; as a result it tends to repeat this response. If the interval before the next presentation is not so great that extinction takes place, a second ‘contingency’ is probable. This strengthens the response still further and subsequent reinforcement becomes more probable. It is true that some responses go unreinforced and some reinforcements appear when the response has not just been made, but the net result is the development of a considerable state of strength.”

SMTM Potato Diet Community Trial: 6 Month Followup

Most diets help people lose a little weight. But once you go off the diet, the weight usually comes right back.

But what about the potato diet? In our recent community trial, people lost an average of 10.6 pounds over only four weeks on the potato diet, and the weight loss was very reliable. Of the people who finished four weeks on the diet, all but one of them lost weight, and a few people lost more than 20 pounds.

Most diets are not nearly this effective. The potato diet seems unusually good at causing weight loss. Could it also be unusually good at maintaining weight loss after people stop eating potatoes? 

There are some signs that it might. The potato diet was partially inspired by several case studies, and the case studies suggest that the weight you lose on the potato diet stays off, at least for a while. We focus on three case studies in particular:

Chris Voigt lost 21 lbs on a 60-day potato diet back in 2010. It’s not clear if he gained that back or not — this article from 2018 doesn’t mention it either way. He looks pretty lean in photos, but then again, he was pretty lean to begin with.

Andrew Taylor did an all-potato diet for a full year and lost 117 pounds. This was 7 years ago and he seems to have kept most of the weight off since then. Of course, Andrew did the potato diet for a full year, and was pretty strict about it, so his experience might not generalize to people who did the potato diet for only four weeks. 

And of course, Penn Jillette, of Penn & Teller fame, lost over 100 lbs on a diet that started with a two-week period of nothing but potatoes. This was way back in 2014, and despite only doing potatoes for two weeks, he seems to have kept most of the weight off as well.

In these cases, especially the last two, it seems like the potatoes have somehow reset these people’s lipostats, the system in the brain that keeps you at a particular weight. Their lipostats used to be really high for some reason; then they did a potato diet; now their lipostat seems to be defending a set point about 100 pounds lower. 

The good news is that we now have a larger sample to work with, so maybe we can finally get at some of these questions. It has been about 6 months since the close of the SMTM Potato Diet Community Trial, and this is the 6-month followup analysis.


We sent an email on January 1st, 2023 to everyone who had participated in the Potato Diet Community Trial, asking people to fill out a short 6-month followup survey.

In this survey, we asked them for:

  • Their potato diet participant ID, so we could connect their responses to the original results
  • Their current weight
  • How much potato they continued to eat post-study
  • If they participated in the SMTM potassium trial
  • And any general comments

We gave people approximately two weeks to fill out this survey. Then on January 14th, we downloaded the data.

There were a total of 53 responses by this point.

The majority (51 of them) were people who we analyzed in the original trial.

Of these, 32 were people who made it the full 4 weeks in the original trial. This happens to be exactly half of the 64 who originally made it to 4 weeks.

When we did the original analysis of the potato diet, there were still a few people who were in the middle of their four weeks of the diet, so we didn’t analyze their data at the time. Two of those people responded to this followup survey. They were not in the original analysis, but they did both complete four weeks, so we are going to include them in this analysis. 

So in total we have 34 people who completed 4 weeks on the potato diet and then reported back at the 6-month check-in. This is our main group of interest.

One person (participant 24235303) reported being 136.4 lbs at the 6-month followup, but he was 222.2 lbs at the end of the potato diet, so this would mean he had lost 85.8 more pounds over the intervening 6 months. Because this seems unlikely, and because his comment was, “my weight drifted back up over a few months”, we assumed this was a typo. We followed up by email and he confirmed that he meant to type 236.4 lbs, so we corrected this number for the analysis. 

Participant 63746180 reported being pregnant (congratulations!) so we are excluding her data from this analysis as her weight may not be representative. 

Participant 65402765 mentioned that they “started semaglutide around the same time as potato diet”. Semaglutide (sold under brand names like Ozempic and Wegovy) is an anti-obesity medication, so while this participant did lose 13.4 lbs in this 6-month period, we also excluded their data from the analysis. 

Because of these exclusions, the final sample size for the rest of the post is 32 people.

All new data and materials are available on the OSF.


On average, people gained back most of the weight they lost. This subset of people lost an average of 11.1 pounds from Day 1 to Day 28, and from Day 28 to the 6-month followup there was on average 10.3 lbs of weight re-gain.

People are on average down 0.71 lbs from their starting weight on Day 1 of the original study, but this is not significantly different from zero. On average, people are pretty much back to baseline.

In aggregate, it looks like a pretty strict reversion to the mean — people lost a little more than 10 lbs over 4 weeks on the potato diet, and gained back almost all of that weight over the next 6 months. 

This is still a relatively successful weight loss intervention — you do a diet for just one month and it takes about 6 months to gain back the weight you lost. This suggests that if you were willing to do a week or two of potato diet every 3 months, you could probably keep your weight down indefinitely.

But just looking at the averages conceals a pretty drastic spread. When we plot the results, we can see that 6 months later, most people are back near baseline, maybe slightly under baseline on average. But some people are down almost 20 or 30 lbs, some people are up more than 10 lbs, and one person is up almost 30 lbs! 

That central cluster is what gives us the average. Most people gained weight in the 6 months after the end of the potato diet, and ended up on average slightly under baseline. 

Four people kept losing weight (one of them isn’t obvious in the plot, they were near the top of the pack at Day 28 and are near the bottom of the pack at the 6-month check-in), and three of those people ended up down more than 15 lbs over 6 months. Those three are the clear outliers below the main group at 6 months.

Five people gained back way more (10+ lbs) than they lost. These are the five dots way above the main group at 6 months, including that one dot that is up at nearly 30+ lbs. 

It may be hopeless to try to figure out what is different about these eight or so people, given the small sample size, but let’s try.


Since there are so few outliers, let’s start by looking at them one-by-one.

Participants ​​99065049, 82575860, 66459072, 10157137, and 77742719 all ended up more than 10 lbs heavier than their baseline on Day 1 of the potato diet. 

Participant ​​99065049 is the outlier, having lost 6.3 lbs in the trial and gained back 34.5 lbs since then, for a total gain of 28.2 lbs since Day 1. We wanted to double-check this result, so we reached out to this participant over email and he confirmed that it was not a typo.

This group didn’t say much about themselves in the comments. Only two of them left responses at all. Participant 10157137 said: 

After the potato diet my cholesterol had improved, but post diet it shot back up again 😔

Participant 82575860 said:

Would appreciate a follow up post on the best potato-based recipes that were sent in 

Participants 20943794, 19289471, and 35182564 lost the most weight. All of them lost more than 5 lbs on the potato diet, and kept losing weight after that. Their total weight loss by 6 months was 19.3 lbs, 23.2 lbs, and 28.7 lbs, respectively. 

Participant 35182564, who lost the most weight, said:

Weight is incredibly stable, although I eat normal, just like before the potato diet. This was a great success.

Participant 20943794 offered the most detail, saying: 

After the potato diet ended, I started a pretty traditional CICO diet using the Noom app. Roughly speaking, I lost 10 lbs on the potato diet, and another 10 on the CICO diet. 

Before the potato diet, I tried calorie counting and various high-protein, low carbohydrate diets, and have never had this kind of sustained success. (E.g., I’ve lost 20 – 30 lbs before, but I didn’t maintain that weight for more than a month or so). 

In addition to the potato diet, there are some other confounding factors: 

1. Whey protein has figured heavily in all my previous diet regimens, but I obviously didn’t take any during the potato diet, and even after it ended, I drastically cut back how much protein powder I consumed (because of the lithium hypothesis) 

2. Because of covid and it’s after-effects, I eat out far less frequently than I ever did before. Since January 2020, I’ve eaten restaurant food (whether dine-in or take-out) only about a dozen times (most of that was on a business trip in October 2022). Before that, I’d say I ate restaurant food on average once per week

Moving on from the comments, we can see if any of the other variables offer us insight.

The potato diet included people from all weight brackets, and maybe that’s what is causing this confusing pattern. For example, maybe all the outliers who gained weight over baseline are people who were slightly underweight when they started the potato diet, and who have gone up to a healthy weight 6 months later. Maybe all the outliers who lost extra weight were very heavy people whose lipostats were easier to reset. 

But when we plot the results by BMI bracket, we see basically no pattern: 

Another possibility is that this reflects whether or not people kept eating potatoes after the trial was over. After all, you can eat potatoes without being on the potato diet, and many people do. Perhaps the people who kept losing weight are the people who stuck with the potato diet, even if only casually, for the long-term. And maybe the people who gained extra weight grew disgusted with potatoes and stopped eating them entirely. 

The good news is that we collected this very variable. But again, when we plot it, we see no such thing: 

The person who lost the most weight ate “way less potatoes than [they] used to”. The people who gained the most weight are all in the middle. No clear pattern here.

That said, if you plot this variable WITHOUT the outliers, you see basically what we would expect — people who kept eating more potatoes are mostly still below their original weight, people who didn’t change their potato intake are back to baseline, and people who are eating way less potato than they used to are slightly above baseline. 

Finally, here’s a breakdown by country. Most participants are Americans but take a look: 

American Holidays

Most of our participants are Americans, and in the span between the start of July and the end of December there’s a major American holiday period that famously involves a lot of eating: the period from Thanksgiving to New Year’s.  

Obligatory Rockwell

As a result, at the 6-month followup our participants were asked to weigh themselves just after a period of especially serious and far-ranging eating. Quite possibly they were being asked to weigh themselves at the heaviest they would be all year.

So in some ways, the particular timing of how this all worked out is a rather conservative test of the potato diet. The weight loss from the potato diet does not seem to survive the holiday period, but it might last somewhat better across any other 6-month span.

A number of our participants commented on this as well. Let’s take a look: 

(57875769) For about the first month after doing the trial my weight continued to trend downward although much more slowly. Then it slowly started creeping back up. Most of the weight came back during the holidays (it’s a little unfortunate that the six month follow up landed right after Thanksgiving, Christmas, and New Years!).

(89852176) After ending the full potato diet about 10 pounds below my typical weight, I returned rather quickly to my baseline (spurred on by eating at family vacation) and stayed there for several months. I ended the year roughly 5 pounds higher than baseline, all of which were gained in the second half of December with “typical” USA holiday (over-)eating.

(63187175) Gained about 5 pounds over the holidays, I was closer to 235 at the beginning of December

(50913144) I stayed at the lower weight for a few months, it only started creeping back up at pre-potato-trial rates in the last 6 weeks or so.  I am probably going to do another round of potato intervention, i don’t like the potassium and it doesn’t seem to help me much. 

(15106191) This measurement is being taken just after the holidays. This is higher than my pre-potato weight but I don’t blame the potatoes, its normal for me to weigh about this much more in January than I did in June

This is also somewhat supported by Nicky Case’s followup survey, which she conducted separately (with our peer review) and ran before the holidays. On October 30th, 2022, she put out a survey on the potato diet, asking people about their current status. She only got 9 responses, but found that most people were still below baseline and had kept most of the weight off.

If we expand our plot using her data, we can see that some people were down quite a bit more in late October / early November than they were at our 6-month check in.  

Some people, however, mentioned gaining the weight back more quickly: 

(25547207) It took about a two months to gain all my weight back. My strength training had to cease 2 weeks in for the remainder of the study, and my large lifts dropped about 10%. It took about 1 month to recover my original strength and I was making gains before fully recovering my weight.

(72706884) I gained back all the weight within 3 months


The potato diet causes very consistent weight loss. But whatever makes the potato diet work doesn’t permanently change your set point. The first thing we see is that most people gain back the weight they lost over time, and on average, it looks like they are back close to their original weight about six months later. 

Unless it did permanently change the lipostat for those three people for some reason. Because the second thing we see is striking individual differences. A small number of people ended up weighing 10+ pounds more or less than they did when they signed up for the trial, and it’s not clear why. 

Maybe they had unusual life circumstances that happened to make them lose or gain more weight over those six months. Maybe they are just random outliers. Or maybe they are more/less sensitive to potatoes for some reason, more sensitive to whatever the active ingredients are. Something something cybernetic attractor states.

There’s a chance that the outliers who kept losing weight are just noise, or that they would have lost weight anyways for some other reason and just happened to sign up for the potato diet at the right time. But there’s also the chance that there is something different about these four participants. If we could figure out what that difference is, maybe we could create lasting weight loss for everyone. For example, are these four people the only four vegans in this sample? We didn’t think to ask this question, but if they were, that would be very interesting. A potential extension then would be to do a much larger potato diet study (1000+ participants) and keep special track of the people who kept losing weight after the trial ended. 

Still, the potato diet is a relatively successful weight loss intervention, since one month of dieting gives consistent results that tend to stick around for about six months. And given the significant individual differences we see, it seems that for some people the effects are more lasting. While we don’t know why this happens for some people and not for others, there’s a small chance that you’ll end up being one of these outliers, and you’ll keep losing weight after the potato diet is over.

We will probably still do the 1-year followup to keep up with these outlier participants, and to see if overall average weight remains below the original baseline or not. But in general, it seems like the conclusion is that 4 weeks of potato diet will make you lose weight, and six months later most people will be back around baseline.

N=1: Single-Subject Research

Previously in this series:
N=1: Introduction


Single-subject or single-case research designs date back to at least the 1980s — though as you can see from the sparse Wikipedia page, they haven’t gotten that much attention. While the idea of single-subject research is good, the execution tends to be crap. 

The simplest single-subject design is the “ABA” design, where you have a few control days (“A”), a few days on the treatment (“B”), and then you go back to the control (“A”). See for example this figure from the University of Connecticut:

In this case, we can see that “frequency of disruptions” (from a target student) is high in the first baseline A block, goes down for a while in the B block (“praise”), and goes back up when they switch back to A.

The ABA design is better than nothing. It’s better than a case study, because at least there’s some experimental control. But it just doesn’t give us all that much information. They have 15 days of data, sure, but only three phases. This is really more like a sample size of three (not that a sample size of 15 is all that much more compelling).

Some sources might recommend the more advanced ABAB approach…

…but ABAB isn’t all that convincing either. Is that a change in the periods of “positive attention”, or just a random walk? It’s pretty hard to tell.

The ABA approach is what Seth Roberts used to argue that his “butter mind” protocol was effective, i.e. that eating more butter made him smarter. Take a look at this ABA graph from one of his self-experiments:

Again, this is vague at best. Yes, it took him longer to do arithmetic problems on 30 g/day butter than on 60 g/day butter. But the change is not very distinct, the three periods clearly overlap, it’s not clear if the periods were randomly determined, etc. And most of all, the sample size is just not very large — we can do better than drawing strong conclusions from a mere three intervals.

You shouldn’t totally discount these simple designs. Starting here can be fine (see for example Allan Neuringer’s self-experiments). Simplicity is important, and if you’re doing early stage exploratory work, going with a study design that’s easy has obvious benefits. If you don’t see any difference with this simple approach, you can move on to something else. If you do see a difference in the ABA, and you want to demonstrate that difference in a more convincing way, then you can expand it into a true within-subjects study. 

There’s nothing wrong with Seth’s butter ABA — it’s just that it looks like the start of something, rather than a conclusion. If he wanted to really convince people, he should have spun that off into a longer self-experiment. 

Within-Subjects Approach

Emily is a woman in her late 20s who gets migraines on a regular basis. She usually gets hit with a migraine in the middle of the afternoon, and they happen almost every day. Emily has been tracking her migraines for a long time, and has determined that each day there’s an 80% chance that a migraine will descend on her at about 2:00 pm, ruining the rest of her afternoon.

Emily has recently noticed that if she takes 400 mg magnesium in the morning, it’s much less likely she’ll have a migraine that afternoon. This isn’t a sure thing — she still gets migraines on days when she takes the magnesium — but it seems like it’s less than 80%.

One way for Emily to get some support for this hypothesis would be for her to run a simple AB self-experiment. She could take no magnesium for two weeks, then take 400 mg magnesium every morning for two weeks, and see if it makes a difference for her migraines. If she gets migraines 80% of the time in weeks where she’s taking no magnesium and only 40% of the time in weeks where she’s taking 400 mg magnesium, that seems like evidence that the magnesium is helping. 

And it is evidence, but it’s on the weak side. Depending on how you slice it, the effective sample size here is two — just one fortnight with magnesium, and one fortnight without. You shouldn’t draw strong conclusions here for the same reason that you shouldn’t draw strong conclusions from a study with one person in the experimental group and one person in the control group. There’s just not that much evidence. 

With small sample sizes, there are too many alternative explanations, it’s too easy to fool yourself. Maybe she started taking magnesium in the springtime, and the increased daylight is the real reason her migraines improved. Or she just started a new job two months ago, and it took her two months to stop grinding her teeth, which happened to align with the switch over to magnesium. Or her detergent manufacturer switched to a new supplier for fragrances. Or maybe her mailman’s cat, which she’s allergic to, ran away last week. It could be almost anything.

To account for these problems, Emily can just go ahead and get a larger sample size. She can use a random number generator to randomly assign days to either take magnesium or not take magnesium, and then follow that random assignment.

With the addition of these two small steps, she can now use a normal within-subjects experimental approach. Let’s imagine she finds that there’s an 80% chance of developing a migraine on days when she takes no magnesium, and a 30% chance of developing a migraine on days when she takes 400 mg magnesium. She can demonstrate this difference to an arbitrary level of precision, just by running the trial for more days.

Emily’s data might look something like this. The data shown here is not quite enough to reach statistical significance (χ2 gives p = .051) but it’s looking pretty good for 30% chance of migraines with magnesium and 80% chance without.

Even if the difference is much more subtle — perhaps a 75% chance of a migraine with 400 mg magnesium and 80% without — with enough days, she can still show to an arbitrary level of confidence that the magnesium has the observed effect FOR HER, however small that effect might be. 

This wouldn’t provide any evidence that magnesium will work for anyone ELSE’s migraines. But even if it doesn’t generalize at all to anyone else, Emily can get as much evidence as she wants that it’s really doing something for her. And that still helps the community, because it shows that the treatment works at least sometimes, for some people. 

This is different from a traditional case study. Even though it’s looking at just one person, it uses experimental techniques. Compared to a traditional experiment, you sacrifice external validity (will this generalize to anyone other than this one person?) but you still get the same level of statistical rigor and you can still clearly infer causality. 

And with this design, you should be able to use standard within-subjects statistical approaches. A sample size of just one is unusual even for a within-subjects study, but not entirely unheard of.

This approach is under-used in the internet research community (though Scott Alexander did one here and Gwern did one with LSD). Lots of people are online sharing tips and tricks on things they think might help their reflux/migraines/IBS/heart palpitations/executive function/etc. This is good, but it’s hard to know which recommendations are solid and which are just random chance. 

If you run a within-subject self-experiment, you can do something incredible for your community. It may not help everyone, but you can demonstrate whether it works for you. Publish your null results too — if you suspect that caffeine triggers your reflux, but under close inspection the hypothesis falls apart, report that shit!

We should emphasize that N = 1 studies falsify a very specific kind of null hypothesis: that an intervention cannot work. If the intervention works for you, that just shows that the intervention can work.

It might not work for anyone else, and with N = 1 you have a much higher chance that one person will do something idiosyncratic that makes it look like the intervention was successful, when in fact it was the idiosyncratic thing doing all the work. For example, maybe on days you take magnesium, you always take it with a big glass of lemonade. It turns out the lemonade is what’s helping your symptoms, not the magnesium, and this wouldn’t be apparent from the data, because the lemonade and the magnesium are confounded.

If you want to go above and beyond, you can get a couple friends and do an even more compelling test with only a few people. As long as you all do multiple trials, your effective sample size from a statistical standpoint can still be arbitrarily large. With more people, we have greater certainty that there isn’t something weird confounded with the experimental variable (but never 100%). Every chronic illness subreddit should be generating research pods of 2-10 people, and testing the treatments they think are worth investigating. 


Even with better randomization, however, these designs still have a lot of limitations.

For a start, they’re limited by the speed of your research cycle. For example, repeated-measures studies won’t work very well for studying obesity, because people tend to lose and gain weight pretty slowly. It may take months to lose and then regain weight, so it’s hard to study weight gain with this method. If you have to randomize periods of months, it will take you a full year to get a sample size of 12. In comparison, headaches would be easier to study, since they come and go daily or even hourly, and you could randomize your treatment on much shorter timescales.

Worse, for some treatments we don’t know what the appropriate timescales will be. Let’s return to Emily and her migraines. If magnesium works on the order of weeks rather than days, she will have to randomly assign weeks rather than days, which means it will take seven times longer to reach an equivalent sample size. But how can she know in advance whether to use periods of days or of weeks?

If a cure is too powerful, or has long-lasting effects, that actually makes it harder to study. If magnesium cures Emily’s migraines for a month, she’ll have to wait a month between randomization cycles, and it will take her years to get a decent sample size.

Similarly, this kind of protocol may not be able to detect more complicated relationships. If Emily’s body builds up a reservoir of extra magnesium over time, this may be difficult to model and might throw off the clarity of the experiment. Or if her body gets more aggressive about clearing the excess magnesium from her system, the magnesium will have less effect over time, and will have even less effect on trial runs where she has multiple magnesium blocks one after another. These designs have a lot to offer, but they’re not going to get us very far in the face of genuinely complex problems.

Another downside of this approach is that Emily has already found a treatment she likes. Probably she would like to take magnesium every day and get as few migraines as possible, but to do this within-subjects self-experiment, she has to try going off the magnesium multiple times over the course of several months, to make sure it really works. We think it’s often worth it to know for sure, but it isn’t easy to stop a treatment that seems to be working.

Finally, the big limitation is that you can only use this approach if you’ve already identified a treatment that you suspect might work for you. If you’re sick and you don’t have any leads, this method can’t help you figure out what to try. It’s only good for testing or confirming hypotheses — it can’t give you any new ones, can’t narrow down a list of cures or triggers out of the huge number of things that might possibly be making you sick.

Low-Dose Potassium at 60 Days

In the SMTM Low-Dose Potassium Community Trial, people took some potassium and lost some weight. Specifically, they took an average of about 1900 mg of potassium per day and lost an average of 0.85 lbs over 29 days

That’s not much weight loss, but it’s also not a very big supplemental dose of potassium, and the weight loss is significantly different from zero. People who took higher doses of potassium lost more weight, as did people who weighed more to begin with.

But what about past that first span of 29 days? Some people kept going with the protocol, taking potassium up to 60 days. Today we report their data.

30+ Days Results

We took a snapshot of all participants’ data on January 5, 2023. This was more than a month after we collected the data from the first 29 days, so everyone had the opportunity to reach 60 days by this point if they wanted to. This new snapshot is available on the OSF.

All the sample sizes in this case are too small to be statistically significant with the potential effect sizes involved, so we don’t report any statistical tests in this post. 

We cleaned these raw data and are going to look at the data from Day 1 on the protocol to Day 60. Some people may have kept going past Day 60, but we aren’t going to look at that right now. 

Here are the overall trajectories for the people who reported at least one day’s weight beyond day 29. The vertical red line indicates day 29, so all data points beyond that are past the span of the original trial. 

Overall the trend seems to continue. One person ended up down more than 15 lbs, but that’s not at all representative. 

People lost weight on average, but we already knew that. In this case we are most interested in whether they kept losing weight past the official end of the trial, so here are those same data zeroed from their weight on Day 29:

We see that in this span, people also lost weight on average, though the average weight loss was not very large. The average weight change past day 29 is negative, -0.37 lbs with all data.

See that spike up to more than 10 lbs? As you may have guessed, those are the days immediately following Thanksgiving. The participant reported that this was their “heaviest weight in 9 years”, but as you can see they lost all that excess weight very quickly. 

These plots can make it hard to see what has happened for each individual, so let’s now break things down and just show their last reported weights, again relative to their weight on Day 29. 

Here’s a plot of each person’s last reported day, and their reported weight change as of that day.

You can see that there are roughly two groups — most people either made it just a few days past Day 29, or made it up to very close to day 60.

We can take a special look at that second group, people who made it to Day 60 or nearly did so. Here’s everyone who made it past 50 days, broken out by just the landmark measurements — their weight on Day 1, on Day 29 at the official end of the trial, and on the last day they reported.

And here are those same data as a table:

On average, these people lost a decent bit (2.7 lbs) in the first span of the trial, and less in the second span (1.0 lbs). But this obscures a lot of individual stories that are more extreme in one way or another, like participant 42293886, who gained 3 lbs in the first leg but lost 4.6 lbs going to day 60, for a total change of 1.6 lbs. (This participant told us, “Not going to go off potassium any time soon I suspect.  Making a little effort to lose weight, and it’s showing a small amount of success.”)

Also notable is that the only two people who had net weight gain by 50+ days are people who had already gained weight by day 29.


Probably the people who kept going past Day 29 were the ones who were most motivated, or who had seen the best results up to that point, so there may be some selection bias.

While none of this is super compelling, people who kept going did on average keep losing weight. They didn’t stick right where they were on Day 29 and they didn’t regress back to the mean. It’s a small amount more evidence in favor of the idea that supplemental potassium might cause weight loss, another tiny pebble on the scale.

In a practical sense, we still recommend that anyone who wants to lose weight should go on the potato or half-tato diet. It’s much more reliable, and more delicious.