If you graph each of the averages calculated in each of the 1,200 sample groups, the resulting shape may result in a uniform distribution, but it is difficult to predict what the actual shape will ...

The Central Limit Theorem is useful when analyzing large data sets because it assumes that the sampling distribution of the mean will be normally distributed and typically form a bell curve.

When particles in a sample are the same size, one particle can be measured to report the result. If the sample has a narrow distribution, such as 10-25 µm, then measurement of just a few particles can ...