Question

what allows us to apply normal calculations to non - normal distributions?
central limit theorem
stratified sample
margins of error
randomness

Explanation:

The Central Limit Theorem (CLT) states that the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution, when the sample size is large enough. This allows normal calculations (such as using z - scores, finding probabilities related to the normal distribution) to be applied to non - normal distributions (by considering the sampling distribution of the sample mean). A stratified sample is a sampling method. Margins of error relate to the precision of estimates. Randomness is a property of a sample selection process. None of these other options directly allow normal calculations on non - normal distributions in the way the CLT does.

Answer:

Central Limit Theorem