The Central Limit Theorem (CLT) states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal, if the sample size is large enough. In other words, if we take enough random samples that are big enough, the proportions of all the samples will be normally distributed around the actual proportion of the population. Note that the underlying sample distribution does not have to be normally distributed for the CLT to apply. To break this down even further, imagine collecting a sample and calculating the sample mean. Repeat this over and over again, collecting a new, independent sample from the population each time. … Continue reading Julia Kho