STATISTICAL CONCEPT

What Is Regression to the Mean? Why Extreme Results Are Followed by Average Ones

You have a great day at work. You expect tomorrow to be great too. It is not. Regression to the mean explains why exceptional performances are followed by ordinary ones.

Editorial illustration showing extreme results returning to average over time
Creator Sir Francis GaltonOrigin StatisticsYear 1886Category Statistics

QUICK ANSWER

Here is the idea in plain English.

Regression to the mean is a statistical phenomenon where extreme results tend to be followed by more average results. It was first identified by Sir Francis Galton in the 19th century. The principle explains why a great baseball season is followed by an average one, why a brilliant test score is followed by a lower one, and why 'hot streaks' are often just statistical noise. It is not about cause and effect. It is about the natural tendency of things to return to average.

If you remember only a few things, remember these.

The basic move

Regression to the mean is simple: extreme results tend to be followed by more average results. If you have a great day, tomorrow will probably be less great. If you have a terrible day, tomorrow will probably be better. The pendulum swings back.

Why it matters

This is not about cause and effect. It is about statistics. Most things are average most of the time. Extreme results are rare. When something extreme happens, it is likely followed by something less extreme.

Use it deliberately

Do not overreact to extremes. Your best day is not your new normal. Your worst day is not your new normal.

CORE IDEA

The concept in its simplest useful form.

What Does Regression to the Mean Mean in Simple Terms?

Regression to the mean is simple: extreme results tend to be followed by more average results. If you have a great day, tomorrow will probably be less great. If you have a terrible day, tomorrow will probably be better. The pendulum swings back.

This is not about cause and effect. It is about statistics. Most things are average most of the time. Extreme results are rare. When something extreme happens, it is likely followed by something less extreme.

The principle explains why you should not overreact to extremes. Your best day is not your new normal. Your worst day is not your new normal. The average is the average.

The small mechanism underneath the big idea.

01

The Story Behind Regression to the Mean

Sir Francis Galton was a Victorian scientist. He studied many things: genetics, psychology, and statistics. One day, he noticed something strange. Tall parents had tall children. But the children were not as tall as the parents. They were closer to average. Short parents had short children. But the children were not as short as the parents. They were also closer to average.

Galton called this 'regression to mediocrity.' He meant that extreme traits tend to regress toward the average. The phenomenon is now called regression to the mean.

The principle is everywhere. Great performances are followed by ordinary ones. Extreme results are followed by more average results. It is not cause and effect. It is statistics.

02

Why Regression to the Mean Became Famous

Regression to the mean became famous because it explains a common mistake: overreacting to extremes. People see a great performance and think it is the new normal. They are wrong.

The concept is essential in statistics, sports, investing, and psychology. It helps people understand that outliers are not trends.

Today, regression to the mean is a fundamental concept in data analysis. It is taught in statistics classes and applied in business, investing, and sports.

Diagram showing extreme values returning to average over time
A diagram showing extreme values trending back toward the average over time.

Where this idea shows up outside the textbook.

Sports

A player has a career year. The next year, they do not repeat it. Regression to the mean is the cause. The career year was an extreme.

Investing

A fund manager has a great year. Investors pile in. The next year, the fund performs average. Regression to the mean is the cause.

Everyday Life

You have a great day at work. You think you have turned a corner. The next day is ordinary. Regression to the mean is the cause.

Academics

A student scores 100% on a test. The next test, they score 80%. Regression to the mean explains it. The 100% was an extreme.

CONCEPT MAP

Every idea has neighbors. This is where the current concept sits in the TinyThat knowledge graph.

Current concept

Regression to the Mean

Extreme outcomes are often followed by more ordinary ones.

Related

Law of Small Numbers

Small samples make extremes look meaningful.

Nearby idea

Base Rate Fallacy

Both correct overinterpretation of standout cases.

Nearby idea

Selection Bias

Extreme samples can mislead.

Nearby idea

Survivorship Bias

Success stories can hide ordinary reversion.

Nearby ideaComing soon

Hot Hand Fallacy

Believing that success will continue.

What people often get wrong about this idea.

Regression to the mean means things always return to average.

No. It means extreme results are followed by more average results. The trend is statistical, not guaranteed.

Regression to the mean is cause and effect.

No. It is statistical. There is no causal mechanism. It is just the nature of probability.

Regression to the mean only applies to sports.

No. It applies to everything: investing, work, relationships, and health. Anything that varies over time.

Useful ideas become dangerous when they are stretched too far.

Criticisms and Limitations of Regression to the Mean

Regression to the mean is a powerful concept, but it has limitations. It does not explain why things happen. It only describes what happens. The concept is a tool, not a theory.

The concept can be misused. Some people use it to dismiss genuine improvement. Not every good result is a statistical fluke. Some improvements are real.

The concept assumes that the underlying distribution is stable. Sometimes the distribution changes. The concept is a guide, not a law.

Three simple ways to apply the idea without turning it into a slogan.

1

Do not overreact to extremes

Do not overreact to extremes. Your best day is not your new normal. Your worst day is not your new normal.

2

When you see a great result, expect a more average one next time

When you see a great result, expect a more average one next time. The extreme is not the trend.

3

When you see a terrible result, expect a better one next time

When you see a terrible result, expect a better one next time. The extreme will not last.

EXPLORE NEXT

The best next ideas to read after this one.

Editorial illustration of a person drawing conclusions from a tiny sample

Law of Small Numbers

Small samples make extremes look meaningful.

Small samples make extremes look meaningful.
Editorial illustration of a person ignoring statistical data in favor of vivid information

Base Rate Fallacy

Both correct overinterpretation of standout cases.

Both correct overinterpretation of standout cases.
Editorial illustration of a person looking at a non-representative sample

Selection Bias

Extreme samples can mislead.

Extreme samples can mislead.
Editorial illustration showing a survivor walking through a graveyard of failures

Survivorship Bias

Success stories can hide ordinary reversion.

Success stories can hide ordinary reversion.

Quick answers to common questions.

What is regression to the mean in simple terms?

Extreme results tend to be followed by more average results. A great day is followed by an ordinary one. A terrible day is followed by a better one.

What is an example of regression to the mean?

A player scores 50 points in one game. The next game, they score 20. Regression to the mean explains it. The 50 points was an extreme.

How do you use regression to the mean?

Do not overreact to extremes. Your best day is not your new normal. Your worst day is not your new normal.

Why is regression to the mean a problem?

It leads to overconfidence or despair. You think extremes are the new normal. They are not.