When you are measuring something that is in the scale of millions, having measures that are close to the mean value doesn’t have the same meaning as when you are measuring something that is in the scale of hundreds. The magnitude of the mean value of the dataset affects the interpretation of its standard deviation. Standard deviation might be difficult to interpret in terms of how large it has to be when considering the data to be widely dispersed. A single extreme value can have a big impact on the standard deviation.
However, standard deviation is affected by extreme values. An item selected at random from a data set whose standard deviation is low has a better chance of being close to the mean than an item from a data set whose standard deviation is higher. The data set with the smaller standard deviation has a narrower spread of measurements around the mean and therefore usually has comparatively fewer high or low values. Standard deviation is useful when comparing the spread of two separate data sets that have approximately the same mean.
