# Bimodal Distribution

Statistical distributions can vary significantly in their shapes, ranging from single-peaked curves like the classic normal distribution to more complex multi-peaked forms such as bimodal. Where a bell curve typically shows concentration of observations around one central value, a bimodal distribution has two distinct peaks – showing that data points are distributed across two separate values.

Statistics can be confusing at times, but understanding the two peaks of a bimodal distribution is more simple than you think. Despite “mode” being used in both statistics and this scenario to refer to numbers with higher frequency, they actually describe different concepts: while mode reflects one single dominant number within data points, a bimodal distribution identifies two local maximums — where values start trending downwards after steadily climbing up on each side before meeting their peaks.

## What a Bimodal Distribution tells you

• You’ve got two peaks of data, which may indicates two different groups. For example, exam scores often have a single peak, indicating that students are of similar ability. However, bimodal distributions can appear in grades sometimes – with many getting As and Fs – which suggests two distinct groups at play. This may be evidence of one group being more prepared than the other: either due to a lack or surplus of prior knowledge.
• Two peaks could also indicate your data is sinusoidal (wave like). If you suspect your data might be following a wave-like pattern, create a scatter or a run sequence plot to double-check for sinusoidal patterns. You could also make a lag plot; an elliptical pattern would confirm that the data is sinusoidal.
• Sometimes, what looks like a bimodal distribution might be two unimodal distributions. For example, this following image shows two separate distributions graphed on the same axes.

## References

Bimodal image: Qwfp at English Wikipedia, CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons