But the true standard deviation of the population from which the values were sampled might be quite different. I'm now doubting the accuracy of this method and have tried to use geometric mean instead. A confidence interval can be computed for almost any value computed from a sample of data, including the standard deviation. 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But the idea of a confidence interval is very general, and you can express the precision of any computed value as a 95% confidence interval (CI). With small samples, this asymmetry is quite noticeable. Find its standard deviation. This calculator will compute the 99%, 95%, and 90% confidence intervals for the mean of a normal population when the population standard deviation is known, given the sample mean, the sample size, and the population standard deviation. Another example is a confidence interval of a best-fit value from regression, for example a confidence interval of a slope. Generally, calculating standard deviation is valuable any time it is desired to know how far from the mean a typical value from a distribution can be. These Excel equations compute the confidence interval of a SD. Or you may have randomly obtained values that are far more scattered than the overall population, making the SD high. For example, in comparing stock A that has an average return of 7% with a standard deviation of 10% against stock B, that has the same average return but a standard deviation of 50%, the first stock would clearly be the safer option, since standard deviation of stock B is significantly larger, for the exact same return. Most people are surprised that small samples define the SD so poorly. Similarly to other mathematical and statistical concepts, there are many different situations in which standard deviation can be used, and thus many different equations. These equations come from page 197-198 of Sheskin (reference below). It is a corrected version of the equation obtained from modifying the population standard deviation equation by using the sample size as the size of the population, which removes some of the bias in the equation. More about the confidence interval for the population variance. This means that the upper confidence interval usually extends further above the sample SD than the lower limit extends below the sample SD.