In this tutorial, you're going to learn about unimodal distributions versus bimodal distributions. This will cover:
Oftentimes distributions will have a clear peak to their shape. They will peak in just one place on the distribution:
They all have a clear peak, so all of these are called a unimodal distributions.
The tallest bar is called the mode.
You might have a distribution that will have two distinct regions with lots of data points, and a gap in the middle. When this happens, the two peaks form on the distribution. And those are both called modes.
A distribution like this is called bimodal.
Technically, there's only one bin that's the mode: the tallest one. But in the above graph, there are two bins that are the tallest relative to the others around them. So they're sort of local modes.
Now sometimes you have a distribution that appears bimodal:
It appears to be bimodal, but upon further examination of heights, it's possible that you have two different distributions that happened to be graphed on the same set of axes:
There might be some hidden variable that causes the bi-modality. When viewed separately, you end up with two unimodal distributions. They just happened to be graphed on the same set of axes.
Any distribution with more than two peaks is called multimodal.
This distribution, for instance, has four peaks:
You can have the same issues with thes, as you did with the bimodal distribution, in that it may be multiple distributions graphed on the same set of axes.
Uni means one, Bi means two, and modal means the number of modes each distribution has.
S distributions are unimodal, or single peaked distributions. Others are bimodal, which means they are clearly double-peaked, and some are multimodal. Sometimes, a bimodal distribution is simply two unimodal distributions graphed together. Oftentimes, there's a reason for the bi-modality.
Thank you and good luck.
Source: THIS WORK IS ADAPTED FROM SOPHIA AUTHOR JONATHAN OSTERS