The experiment is to generate a random sample \(\boldsymbol{X} = (X_1, X_2, \ldots, X_n)\) from the normal distribution with mean \(\mu\) and standard deviation \(\sigma\). The statistics of interest are \begin{align} M &= \frac{1}{n} \sum_{i=1}^n X_i \\ S^2 &= \frac{1}{n-1} \sum_{i=1}^n (X_i - M)^2 \\ T^2 &= \frac{1}{n} \sum_{i=1}^n (X_i - M)^2 \\ W^2 &= \frac{1}{n} \sum_{i=1}^n (X_i - \mu)^2 \end{align} The statistic \(M\) is the sample mean and is a point estimator of the distribution mean \(\mu\). The statistics \(S^2\), \(T^2\), and \(W^2\) are sample variances and are point estimators of the distribution variance \(\sigma^2\). (For \(W^2\), we must assume that \(\mu\) is known.) The parameters \(\mu\), \(\sigma\), and \(n\) can be varied with the input controls.