In statistics, when faced with a set of data, we can observe how often each value appears. The value that appears most frequently is called the mode. But, what happens when there are two values that share the same frequency in the set? In this case we are dealing with a bimodal distribution.
Example of bimodal distribution
An easier way to understand the bimodal distribution is to compare it with other types of distributions. Let’s look at the following data in a frequency distribution:
1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5, 6, 6, 6, 7, 7, 7, 8, 10, 10
By counting each number we can conclude that number 2 is the one that is repeated most frequently, a total of 4 times. We have then found the mode of this distribution.
Let’s compare this result with a new distribution:
1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5, 6, 6, 6, 7, 7, 7, 7, 7, 8, 10, 10, 10, 10, 10
In this case, we are in the presence of a bimodal distribution since the numbers 7 and 10 occur a greater number of times.
Implications of a bimodal distribution
As in many aspects of life, chance plays an important role in the distribution of elements, and for this reason statistical parameters must be used that allow us to study a data set and determine patterns or behaviors that provide us with valuable information. The bimodal distribution provides a type of information that can be used in conjunction with the mode and median to study in depth natural or human phenomena of scientific interest.
Such is the case of a study on precipitation levels in Colombia, which yielded a bimodal distribution for the northern zone, which includes the departments of Caldas, Risaralda, Quindío, Tolima and Cundinamarca. These statistical results allow us to study the great heterogeneity of topoclimates present in the Colombian Andean cordilleras from the establishment of patterns in the natural phenomena of these regions. This study represents an example of how statistical distributions are used in practice for research.
Jaramillo, A. and Chaves, B. (2000). Precipitation distribution in Colombia analyzed through statistical conglomeration. Cenicafé 51(2): 102-11
Levin, R. & Rubin, D. (2004). Statistics for Administration. Pearson Education.
Manuel Nasif. (2020). Unimodal, bimodal, uniform mode. Available at https://www.youtube.com/watch?v=6j-pxEgRZuU&ab_channel=manuelnasif