Abstract
Standard deviation and standard error have a clear mutual relationship, but at the same time they differ strongly in the type of information they supply. This can lead to confusion and misunderstandings. Standard deviation describes the variability in a sample of measures of a variable, for instance the variability in ages of the members of a group. It represents the degree to which the values are scattered around their mean: the higher the standard deviation the wider the spread. The value of the standard deviation is not influenced by the number of observations in the sample. The standard error is always used for extrapolation: to estimate the intervals between which the true value of a statistic will occur, based on a sample of observations and with a certain degree of certainty. When interpreting a standard error, it is important to know which statistic (mean, percentage, relative risk, odds ratio) is to be estimated. The value of the standard error is strongly influenced by the number of observations in the sample: the bigger the sample, the smaller the standard error and the more accurate the estimation. To avoid confusion it is recommended to report no longer the standard error of the mean but instead the confidence intervals of the mean to estimate the true value of the mean.
