The Two Metrics That Reveal True Data Dispersion Beyond Standard Deviation

<h1>Introduction</h1> We’ve all heard the saying, “Variety is the spice of life,” and in data, that variety or diversity often takes the form of dispersion. Data dispersion makes data fascinating by highlighting patterns and insights we wouldn’t have found otherwise. Typically, we use the following as measures of dispersion: variance, standard deviation, range, and interquartile range (IQR). However, we may need to examine dataset dispersion beyond these typical measures in some cases. This is where the Coefficient of Variation (CV) and Quartile Coefficient of Dispersion (QCD) provide insights when comparing datasets. In this tutorial, we will explore the two concepts of CV and QCD and answer the following questions for each of them: <ul> <li>What are they, and how are they defined?</li> <li>How can they be computed?</li> <li>How to interpret the results?</li> </ul> All the above questions will be answered thoroughly and through two examples. <a href="https://towardsdatascience.com/dispersion-cv-qcd-32849f828434">Website</a>