In
statistics, the
standard deviation is a measure of the amount of variation or
dispersion of a set of values.
[1] A low standard deviation indicates that the values tend to be close to the
mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.
The standard deviation of a
random variable,
statistical population,
data set, or
probability distribution is the
square root of its
variance. It is
algebraically simpler, though in practice less
robust, than the
average absolute deviation.
[2][3] A useful property of the standard deviation is that, unlike the variance, it is expressed in the same units as the data.
In addition to expressing the variability of a population, the standard deviation is commonly used to measure confidence in statistical conclusions. For example, the
margin of error in
polling data is determined by calculating the expected standard deviation in the results if the same poll were to be conducted multiple times. This derivation of a standard deviation is often called the "
standard error" of the estimate or "standard error of the mean" when referring to a mean. It is computed as the standard deviation of all the means that would be computed from that population if an infinite number of samples were drawn and a mean for each sample were computed.
The standard deviation of a population and the standard error of a statistic derived from that population (such as the mean) are quite different but related (related by the inverse of the square root of the number of observations). The reported margin of error of a poll is computed from the standard error of the mean (or alternatively from the product of the standard deviation of the population and the inverse of the square root of the sample size, which is the same thing) and is typically about twice the standard deviation—the half-width of a 95 percent
confidence interval.
In science, many researchers report the standard deviation of experimental data, and by convention, only effects more than two standard deviations away from a null expectation are considered
statistically significant—normal random error or variation in the measurements is in this way distinguished from likely genuine effects or associations. The standard deviation is also important in finance, where the standard deviation on the
rate of return on an investment is a measure of the
volatility of the investment.
When only a
sample of data from a population is available, the term
standard deviation of the sample or
sample standard deviation can refer to either the above-mentioned quantity as applied to those data, or to a modified quantity that is an unbiased estimate of the
population standard deviation (the standard deviation of the entire population).