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The population and sample standard deviation are important concepts in the world of statistics. Population means the entire set of all items or observations you’re trying to study; sample is a subset of those items or observations. Standard deviation is the measure of how spread out numbers are from what we would call an “average number”.
Population and sample standard deviation are useful tools for understanding how closely data points cluster around the mean. When data is more scattered, there may be outliers. Outliers can skew the average of the data, so it is important to qualify any findings with an analysis of outliers.
Sample standard deviation is a measure of variability in a given set of data, such as test scores or stocks. Sample standard deviation (or sigma) is a useful statistical tool that indicates the average distance that observations in a population are from their mean value. Essentially, it is a measure of how closely all of the observations in a set are clustered around the mean.
Population standard deviation is the statistical measure used to assess variation in a population. It is defined as the square root of the variance, which is the average squared deviation from the mean. Standard deviations are typically calculated for continuous distributions, but can also be calculated for discrete data.