<P> The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation . If the parameter or the statistic is the mean, it is called the standard error of the mean (SEM). </P> <P> The sampling distribution of a population mean is generated by repeated sampling and recording of the means obtained . This forms a distribution of different means, and this distribution has its own mean and variance . Mathematically, the variance of the sampling distribution obtained is equal to the variance of the population divided by the sample size . This is because as the sample size increases, sample means cluster more closely around the population mean . </P> <P> Therefore, the relationship between the standard error and the standard deviation is such that, for a given sample size, the standard error equals the standard deviation divided by the square root of the sample size . In other words, the standard error of the mean is a measure of the dispersion of sample means around the population mean . </P> <P> In regression analysis, the term "standard error" refers either to the square root of the reduced chi - squared statistic or the standard error for a particular regression coefficient (as used in, e.g., confidence intervals). </P>

How do you find the standard error of the sample mean
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