Explanations of central tendency measures

A measure of dispersion provides us with a suggestion of how good the mean is representative of the data (Robert and Patricia, 2011). If the values of a data are extensively dispersed, then the mean is not representing the data precisely, while if the dispersion is small, then the mean is a good representation of the data. Therefore, of the two diet plans, since they have same mean, the large dispersion of weight loss in diet plan B is an indication that there are credible huge variations between personal scores than in diet plan A. Hence, diet plan A has a positive effect on individuals weight loss while diet B has a negative effect. This is because individuals lost weight almost similarly in diet plan A, while in Diet plan B individuals lost weight unevenly. An indication that diet plan B had a positive effect on some individuals while others had a negative effect. A little variation in weight loss in diet plan A indicates that all individuals in that group lost weight similarly making this diet most effective. This is the reason why the measure of dispersion is vital, especially in views with its relationship with mean as a measure of central tendency.