People are often confused by confidence intervals, and even dyed in the wool statisticians get the definition mixed up from time to time. However, I’ve found that if you derive them with bootstrapping, they’re meaning becomes crystal clear.

However, before we start, I want to say that there are a lot of ways to create confidence intervals. Bootstrapping is just one way, but usually people create them using formulas. The formulas are fine, but I don’t think they are as easy to understand.

If you can’t remember bootstrapping, I review it briefly in the video for confidence intervals, or you can read my post on the standard error.

The basic idea is that you take a sample of something, apply bootstrapping to it, and then create an interval that covers 95% of the means. That’s all! Here are some figures to illustrate it.

- First, take a sample. Here’s a sample of weights from 12 female mice.
- Bootstrap the sample.
- Create an interval that covers 95% of the samples (this will be a 95% confidence interval).

Now that we know what a confidence interval is, why should we care? Well, I like confidence intervals because they let us do statistics using pictures, rather than equations. It’s nice when you can just look at something and say, “The p-value for this hypothesis is less than 0.05, thus, we will reject the hypothesis” without having to rely on a single equation.

Here’s an example. In that figure, all values outside of the 95% confidence interval occurred less than 5% of the time. Thus, the p-value of the true mean from the population taking on any of the values outside of the confidence interval has a p-value < 0.05.