Unimodal Distribution vs Bimodal Distribution
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Unimodal distribution
Describing data
A unimodal distribution has a single clear peak, so its graph rises to one high point and falls away on both sides of it.
Unimodal is a statement about shape rather than center: it says the values cluster around one location instead of two or more. You judge it from a graph by counting the major peaks and ignoring small bumps that come from random wobble in the counts. For example, a dotplot of resting heart rates for 60 adults might pile up near 70 beats per minute and thin out in both directions, which you would call unimodal. A graph with two distinct peaks is bimodal instead, and one with no peak at all is roughly uniform.
Bimodal distribution
Describing data
A bimodal distribution has two distinct peaks, showing two values or ranges where observations cluster most heavily.
A bimodal shape has two clear humps, which often signals that the data mix two different groups. For example, heights measured from a room of both children and adults may peak once near each group's typical height. Each peak is a mode, so two peaks means two modes, in contrast to a single-peaked (unimodal) distribution. When you see two modes, it is worth asking whether the data should be split into subgroups.