CLT Approach

The CLT (Central Limit Theorem) approach explains that when you take many random samples from a population and calculate their averages, these averages tend to form a normal (bell-shaped) distribution, regardless of the original population's shape. This means that for large enough samples, the distribution of sample means becomes predictable, allowing us to make confident inferences about the population. Essentially, the CLT provides a foundation for techniques like estimating averages or testing hypotheses, by ensuring that the sample averages behave in a consistent and understandable way as the number of samples increases.