Standard Deviation as Measure of Dispersion Explained
The standard deviation is a measure of dispersion, more precisely a measure of how much the data differ from the mean. Suppose that the mean height and weight of adult males in the US are 5'10" (70 inches) and 170 lbs., respectively. No man is 50% taller than the average, i.e. no man is taller than 105 inches, which is 3 inches "shorter" than 9 feet. Many men are more than 50% heaver than the average, i.e. heavier than 255 lbs. Quite a few are heavier than 300 lbs. Thus, weights vary from the average more than heights at the right tail. Therefore, the standard deviation of weights is higher, relative to the mean, than the standard deviation of heights. Heights are more symmetrically distributed about the mean.
Suppose that the most common suit size of adult males in the US is 42. Suit size corresponds to chest girth. It is likely then that the mean weight of men who wear a size 42 suit is nearly 170 lbs. It is most likely less than the mean weight of all adult men because there are many more 240+ pounders, who will not fit into a size 42, than 100- pounders, who will also not fit into a size 42.
Suppose that the the standard deviation of weights of adult males in the US is 15 lbs. It is likely that the standard deviation of weights of men who wear a size 42 suit is less than 15 lbs, say 10 lbs or as little as 5 lbs. This is because very light men and very heavy men will not fit into a size 42 suit and the weights of men who wear a size 42 suit are less dispersed about 170 lbs. than the weights of all American adult males, which includes jockeys and feather weight boxers as well as linebackers and basketball players.