What defines a bimodal distribution?

A bimodal distribution is characterized by having two distinct peaks or modes in its frequency distribution. This means that when the data is plotted, you will observe two separate values where data points are concentrated, each representing one of the modes.

The presence of these two modes can indicate that the data has two underlying processes or categories that are contributing to the data set. For instance, if you're analyzing test scores from two different classes that were taught differently, the scores might cluster around two distinct values, creating a bimodal distribution.

The other options do not apply to bimodal distributions; a single distinct mode represents a unimodal distribution, while multiple modes describe a multimodal distribution. Having an identical mean and median is a property often associated with symmetric distributions, particularly normal distributions, but does not define bimodal distributions specifically. Understanding the characteristics of bimodal distributions is crucial, as it can provide insights into the underlying factors influencing the data.