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 ...

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.

Stratified random sampling is a method of sampling that divides a population into smaller groups that form the basis of test samples.

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.

Mary C. Christman, A Review of Quadrat-Based Sampling of Rare, Geographically Clustered Populations, Journal of Agricultural, Biological, and Environmental Statistics ...