Sampling distribution: Sampling distribution of a statistic is the frequency distribution which is formed with various values of a statistic computed from different samples of the same size drawn from the same population.
For instance if we draw a sample of size n from a given finite population of size N, then the total number of possible samples is NCn = N!/n!(N - n)! = k (say). For each of these k samples, we can compute some statistic, t = t(x1 , x2 , x3 ,… xn ), in particular the mean \(\overline{X}\) the variance S2 , etc., is given below
The set of the values of the statistic so obtained, one for each sample constitutes the sampling distribution of the statistic.
Standard Error: The standard deviation of the sampling distribution of a statistic is known as its Standard Error abbreviated as S.E. The Standard Errors (S.E.) of some of the well-known statistics, for large samples, are given below, where n is the sample size, σ2 is the population variance.
