A thought experiment about sampling distributions: Imagine you take a random sample of individuals from a target population, measure something and then calculate a sample statistic, the “mean” let’s ...

True experiments, unlike anecdotal evidence, often require random sampling and random assignment. In this post, I try to explain the importance of random sampling; in my next post, I will explore ...

Central Limit Theorem: A sampling distribution of the mean is approximately normally distributed if the sample size is sufficiently large. This is true no matter what the population distribution is.

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.