Misleading characterization of data.

a, Frequently, variation in data from across the sciences is characterized with the arithmetic mean and the standard deviation SD. Often, it is evident from the numbers that the data have to be skewed. This becomes clear if the lower end of the 95% interval of normal variation, - 2 SD, extends below zero, thus failing the “95% range check”, as is the case for all cited examples. Values in bold contradict the positive nature of the data. b, More often, variation is described with the standard error of the mean, SEM (SD = SEM · √n, with n = sample size). Such distributions are often even more skewed, and their original characterization as being symmetric is even more misleading. Original values are given in italics (°estimated from graphs). Most often, each reference cited contains several examples, in addition to the case(s) considered here. Table 2 collects further examples.