The measure of variability is highly used in mathematics, and it refers to a summary statistic that helps determine the degree of variability that is found in any given data ( Allaj, 2018) . In other words, the measure of variability determines how far apart or how spread the values of the dataset are. If there is low variability, then it means that the data values are close together around the center. In contrast, high variability means that the data values are further apart from the center. The most common measures of variability are the variance, range, standard deviation, and the interquartile range.
Variance measures how far values are from the mean by obtaining the differences from the mean, then squaring the difference, and then averaging ( Allaj, 2018) . The range, on the other hand, is the difference between the maximum and minimum values. The standard deviation is the square root of the variance, while the interquartile range seeks to determine where most of the values lie. The measures of variability differ from the measures of central tendency though the two are quite important in statistics.
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The main difference between the two is that the measures of variability determine how spread values are while the measures of central tendency like the mean determine the central location of the data ( Kołacz & Grzegorzewski, 2016) . These two go hand in hand in descriptive statistics, and they give a good summary of the dataset in question. These two can, therefore, provide very important characteristics of the dataset that can help conclude the data.
Measures of variability are highly applicable in almost all areas ( Kołacz & Grzegorzewski, 2016). One good application in business is their use in determining the range of wages that are given to employees in the company. Additionally, the measures of variability, especially variance, are also helpful in determining the variance of the wages of employees in the company.
References
Allaj, E. (2018). Two simple measures of variability for categorical data. Journal of Applied Statistics , 45 (8), 1497-1516.
Kołacz, A., & Grzegorzewski, P. (2016). Measures of dispersion for multidimensional data. European Journal of Operational Research , 251 (3), 930-937.
