Measure of the dispersion or variability of a set of values. Defined mathematically, it is the square root of the variance of these observations. By definition, approximately 68% of the values in the normal distribution (or bell‐shaped curve) fall within 1 SD on either side of the mean. If the SD exceeds one half the mean, the data is not normally distributed.
A measure of the variability of a frequency distribution that is the square root of the variance.
A measure of the scatter of observations about their arithmetic mean, which is calculated from the square root of the variance of the readings in the series. The arithmetic sum of the amounts by which each observation varies from the mean must be zero, but if these variations are squared before being summated, a positive value is obtained: the mean of this value is the variance. In practice a more reliable estimate of variance is obtained by dividing the sum of the squared deviations by one less than the total number of observations.
A statistical measure of the spread of observations about their arithmetic mean. It is a measure regularly used in working out the results of trials of clinical treatment. Simplistically, anything above and below 2 standard deviations from the norm is often regarded as potentially abnormal, in that only about 5% of the population will be in that area.
In statistics, the commonly used measure of dispersion or variability in a distribution; the square root of the variance.
The concept of measuring the dispersion of scores in relation to the average, commonly known as the mean, provides valuable insights. In a normally distributed dataset, approximately 95% of all samples can be found within two standard deviations above and below the mean.