## Table of contents

### What is a Measure of Central Tendency?

A measure of central tendency is a single value that describes the way in which a set of data cluster around a central value. In other words, it is a way to describe the center of a data set.

The three main measures of central tendency are:

### Mode of the Distribution

The mode is a measure of central tendency that represents the most frequently observed value or range of values in a set of data. If there are two separate values that appear most often, the set is said to be “Bi-modal”.

### When to use Mode as a Measure of Central Tendency?

The mode is useful when the set of data:

- has more than one segment (or multi modal)
- is badly skewed, or
- it is necessary to eliminate the effect of extreme values.

### Examples of variables with bimodal distributions:

Examples of variables with bimodal distributions include the time between eruptions of certain geysers, the color of galaxies, the size of worker weaver ants, the age of incidence of Hodgkin’s lymphoma, the speed of inactivation of the drug isoniazid in US adults, the absolute magnitude of novae, and the circadian activity patterns of those crepuscular animals that are active both in morning and evening twilight. In fishery science multimodal length distributions reflect the different year classes and can thus be used for age distribution- and growth estimates of the fish population. ^{}Sediments are usually distributed in a bimodal fashion. Bimodal distributions are also seen in traffic analysis, where traffic peaks in during the AM rush hour and then again in the PM rush hour. This phenomenon is also seen in daily water distribution, as water demand, in the form of showers, cooking, and toilet use, generally peak in the morning and evening periods. Wikipedia, Multimodal distribution (https://en.wikipedia.org/wiki/Multimodal_distribution)

### What are your thoughts?

If you have any examples of using the Mode as a measure of central tendency, please tell us in the comments below.