CLT

The Central Limit Theorem (CLT) states that when you take many random samples from any population—regardless of its shape—and calculate their averages, these averages tend to form a normal (bell-shaped) distribution. This happens as the sample size gets larger. Essentially, the CLT explains why the distribution of sample means is approximately normal, which allows us to make inferences and confidence statements about the overall population even if the original data isn’t normally distributed. It’s a fundamental principle that underpins much of statistical analysis and probability.