In our last blog, we talked about averages and variability—how to describe what’s typical and how much your data varies. But there’s something else that matters before jumping into analysis:

What shape is your data?

Today we’ll look at the normal distribution—the famous “bell curve”—and why it’s so important in research. Don’t worry, no math. Just a simple explanation of how this curve helps us make decisions from data.

What’s the Normal Distribution, Really?

A normal distribution is a smooth, symmetrical, bell-shaped curve. Most values are bunched up near the center, and fewer appear as you move out to the extremes.

In medicine, a lot of variables—like blood pressure, cholesterol, height, or heart rate—tend to follow this pattern, especially when measured in large samples from the general population.

The beauty of this shape? It’s predictable and well-behaved, which makes it perfect for many of the statistical tests we use in research.

The Role of Symmetry and Sample Size

Here’s the key point:

As long as your data is roughly unimodal and symmetrical—even if it’s not perfectly shaped like a bell—the usual statistical tests tend to work just fine, especially when you have enough data.

And that’s thanks to the Central Limit Theorem.

What’s the Central Limit Theorem?

In plain terms:

If you take enough samples (usually 30 or more in each group), the distribution of the averages will tend to be normal—even if the data itself isn’t perfectly normal.

That’s why most tests (like the t-test or ANOVA) are considered robust. As long as the sample size is large and the distribution isn’t wildly irregular, they perform well.

When the Data Isn’t Symmetrical

Sometimes, your data might not be so well-behaved. Maybe it has a long tail or a few extreme values. That’s called skewness, and in those cases:

• It may be better to use the median instead of the mean

• You might need non-parametric tests that don’t assume a normal distribution

• And later in this series, we’ll show you how to transform data to make it more symmetrical if needed

But for now, just know this:

You don’t need perfect data to use common statistical tools—just a reasonably symmetric shape and enough participants per group.

Key Takeaways

• The normal distribution is a bell-shaped curve where most values are near the center.

• Many statistical tests assume data is approximately normal.

• Thanks to the Central Limit Theorem, if your sample size is 30 or more per group, most tests still work—even if the data isn’t perfectly normal.

• If your data is skewed, you may use medians, non-parametric tests, or even transform the data (we’ll cover that later).

  
  

Up Next:

Next, we’ll dive into probability, p-values, and confidence intervals—the tools we use to decide whether a result is real or just due to chance.