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  • Methodology
  • robust evidence
  • 4 min · 4 sources
  • Published Mar 22, 2026

Regression to the Mean

Extreme results often move closer to normal when measured again.

It can keep you from crediting a supplement, coach, or plan for a rebound that would have happened anyway.

Also known as regression toward the mean · RTM · regression-to-the-mean effect · regression to the mean fallacy

Regression to the Mean in brief

  • Extreme observations contain more random noise, so repeat measurements usually move closer to the average.1
  • The concept matters most when judging supplements, coaching, therapy, or self-experiments started after a bad run.
  • The common mistake is confusing ordinary rebound with a true intervention effect.

What Regression to the Mean means

The trap hides inside dramatic before-and-after stories

The easiest time to get fooled by regression to the mean is exactly when a story feels most convincing: my sleep was awful, I started magnesium glycinate, and three nights later I felt much better. That improvement may be partly real. But if you began during an unusually bad stretch, you also chose a moment that was statistically likely to be followed by something less extreme anyway.

That is why regression to the mean shows up everywhere from regression to the mean in psychology studies to sports “slumps,” blood pressure follow-ups, and supplement self-trials. Sir Francis Galton noticed it when very tall parents tended to have children who were still tall, but not usually quite as extreme as the parents.

The rubber-band picture

Picture a basketball tossed far above the crowd. The next bounce is usually still high, just not that high. Regression to the mean works like that with repeated measurements: when a result is extreme because of both a stable signal and a temporary wobble, the wobble is unlikely to be extreme in the same direction twice.

That “temporary wobble” can be many ordinary things: random biological fluctuation, measurement noise, a bad night of sleep, unusual stress, lucky guessing, or just having a weird day. The key surprise is this: regression to the mean is not a force pushing things back to average. It is what you expect when you re-measure something imperfectly stable after selecting an extreme result.

So what is meant by regression to the mean? In plain language: if you pick people, days, or scores because they were exceptionally high or low, the next measurement will usually be closer to typical. Not always. Not fully. Just closer on average.

Where the fallacy enters

The regression to the mean fallacy happens when people treat that ordinary softening as proof that some intervention worked or failed. A coach praises an athlete after a terrible game; the next game is better, so the coach thinks the speech caused it. A parent scolds a child after an unusually reckless day; the next day is calmer, so the scolding looks magically effective. Kahneman used a version of this training-story mistake to show how easy it is to confuse fluctuation with causation.

One decision that helps today

If you are testing a supplement, do not judge it from the week that made you desperate enough to start. Judge it against a longer baseline and, if possible, repeated weeks rather than one dramatic before-and-after swing. That single decision will protect you from a huge share of fake “wins” and fake “failures.”

Deep dive

How it works

In statistical terms, regression to the mean appears when repeated measures are correlated but not perfectly correlated. If the correlation between first and second measurements is less than 1, the expected second score for someone selected on an extreme first score lies closer to the population mean than the initial score. The weaker the correlation, the stronger this pull toward a less-extreme expected follow-up.

When you'll see this

The term in the wild

  1. You start magnesium glycinate after your worst three nights of sleep in months, then sleep improves that week.

    Because you began at an extreme low point, some rebound toward your usual sleep was likely even without the supplement.

    Without a longer baseline, you may over-credit the supplement and miss how much ordinary fluctuation was involved.

  2. A psychology study recruits students only after unusually high anxiety scores, then re-tests them two weeks later.

    Even if nothing changes, the average follow-up score will often be lower because the first selection captured many temporarily high readings.

    This is why uncontrolled before-and-after designs can make weak interventions look stronger than they are.

  3. A pitcher appears on the cover of Sports Illustrated after a spectacular streak, then plays less brilliantly the next month.

    Fans may call it a 'jinx,' but extreme hot streaks are hard to sustain; later performance often moves closer to the player's typical level.

    Regression to the mean can look like fate, pressure, or a curse when it is really a predictable statistical pattern.

Myths vs reality

What people get wrong

  • Myth

    Regression to the mean means things always return to average.

    Why people believe it The word 'regression' sounds like a built-in pull backward, so people hear a law of decline instead of a pattern in repeated measurements.

    Reality

    No. It means selected extremes tend to be followed by less-extreme results on average. A great athlete can stay great; the next result is just less likely to be wildly exceptional again.

  • Myth

    If a score improves after treatment, the treatment must have worked.

    Why people believe it Before-and-after designs without a control group are common in wellness marketing, self-tracking, and low-quality studies, so normal fluctuation gets mistaken for causation.

    Reality

    Not necessarily. If you started treatment on a terrible day, improvement may partly reflect ordinary rebound from an extreme reading.

  • Myth

    Regression to the mean is just a fancy excuse skeptics use to dismiss real effects.

    Why people believe it Named stories like the Sports Illustrated 'cover jinx' and Kahneman's Israeli Air Force training anecdote make the idea feel like a debunking trick instead of a basic research safeguard.

    Reality

    It does not erase real effects; it prevents you from exaggerating them. A treatment can help and regression to the mean can still be part of the observed change.

Putting Regression to the Mean to work

A common failure mode in self-experimentation is starting three things at once right after a crisis week, say magnesium, earlier bedtime, and less caffeine, then crediting whichever change you liked best. When the starting point is extreme, regression to the mean makes that kind of pile-on especially misleading.

Frequently asked

Common questions

What does regression to the mean refer to?

It means unusually high or low results are likely to be followed by results that are closer to typical. The effect shows up when measurements are noisy and you focus on extreme cases.

What counts as a regression to the mean fallacy?

Starting a supplement on your worst week, then assuming the next better week proves it worked, is a classic example. Some improvement may be real, but some may simply be the ordinary rebound from starting at an extreme low point.

What is regression to the mean in psychology?

In psychology, it often appears when people are selected because of very high or very low scores, such as anxiety, depression, or test performance, and then score less extremely on retesting. Without a control group, that shift can be mistaken for a treatment effect.

Is there a regression to the mean formula?

Yes, formal statistics can express it using correlations and expected values, but the practical idea is simpler: the less-than-perfect link between two measurements makes repeat scores less extreme on average. You do not need the formula to avoid the mistake.

How do researchers reduce the problem?

They use control groups, repeated measurements, randomization, and pre-specified analysis plans. Those design choices help separate genuine change from the normal softening of extreme observations.

Sources

Sources

  1. 1. Regression towards the mean (1994)
  2. 2. Some examples of regression towards the mean (1994)
  3. 3. Regression toward mediocrity in hereditary stature
  4. 4. Thinking, Fast and Slow (2011)

Related

Where this term shows up

Evidence guides and other glossary entries that touch this concept.