![]() ![]() In the latter case, a sample of data from such a distribution can be used to construct an estimate of the variance of the underlying distribution in the simplest cases this estimate can be the sample variance. ![]() ![]() The variance is a parameter that describes, in part, either the actual probability distribution of an observed population of numbers, or the theoretical probability distribution of a not-fully-observed population from which a sample of numbers has been drawn. While other such approaches have been developed, those based on moments are advantageous in terms of mathematical and computational simplicity. In that context, it forms part of a systematic approach to distinguishing between probability distributions. The variance is one of several descriptors of a probability distribution, In particular, the variance is one of the moments of a distribution. How does this happen If data have a very skewed distribution, then the standard deviation will be grossly inflated, and is not a good measure of variability to. Variance is always non-negative, a small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out around the mean and from each other. A variance of zero indicates that all the values are identical. Variance measures how far a set of numbers is spread out. ![]()
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