Regression to the Mean

What is Regression to the Mean?

Regression to the mean happens when you observe an extreme measurement, either unusually high or low, and the next measurement naturally moves closer to the average.

It’s a built-in property of statistics. Most observations cluster around the mean. So, if something out of the ordinary happens, odds are the next thing you observe will look more typical.

Imagine you run an A/B test and the first few days show an unusually high conversion rate for a new variant. Exciting, right? But a week later, the numbers drop closer to normal. That doesn’t automatically mean your change was bad. It could just be regression to the mean doing its thing.

How Regression to the Mean Affects Experiments

If you don’t account for regression to the mean, you risk misreading natural fluctuation as the effect of your test.

Say you spot a sudden spike in revenue and immediately launch a new feature, believing it caused the boost. Later, revenue slides back to its usual level. Without understanding regression to the mean, you might wrongly think the feature caused the drop—or worse, you could have celebrated a random spike as a real win.

This is why it’s dangerous to act on early signals or one-off observations without proper analysis.

“In statistics, the tendency to move back towards the mean is called regression to the mean. It happens because extreme events are usually followed by more typical ones. Since most values are near the average, it’s much more likely to get an average number than another extreme one.

Suppose we have a distribution from 1 to 100, with a mean value of 50. If we pick a random number from this distribution and get an extreme value, like 95, and then pick another number, the second number will likely be less extreme and closer to the mean. That’s because there’s a 95% chance of picking a smaller number on the second draw, which would naturally be closer to the average.

It’s important to remember that in data science, sometimes, big changes we see in data may naturally “regress” without any real reason. Hence, we need to be careful when analyzing results.”

Gustavo R Santos, Data Scientist

Common Mistakes Related to Regression to the Mean

Teams often fall into these traps when they forget about regression to the mean:

• Thinking a positive or negative spike is entirely because of their intervention.
• Launching or killing features too soon based on a short-term swing.
• Overreacting to small experiments or low-sample tests where random noise plays a bigger role.

Have you ever gotten excited about early A/B test results, only to see them “normalize” later? That’s a classic sign.

Best Practices to Guard Against It

Here’s how to keep regression to the mean from fooling you:

• Plan experiments carefully: Use proper randomization to avoid starting with a weirdly lucky or unlucky sample.
• Get enough data: A small sample size makes natural swings look bigger than they are.
• Demand statistical significance: Wait until the math says a result is likely real, not just a blip.
• Run A/A tests: Regularly running tests where nothing is supposed to change can show you what normal fluctuation looks like.
• Stay skeptical of extremes: If something looks too good—or too bad—dig deeper before making decisions.

Good experimenters don’t chase every spike. They stay patient and keep their eye on the bigger pattern.