The Central Limit Theorem is a fundamental concept in statistics that states, no matter the shape of the original population distribution, the sampling distribution of the sample mean will be approximately normally distributed if the sample size is large enough. This theorem is crucial in hypothesis testing and confidence intervals as it allows us to make inferences about a population based on a sample. To illustrate this, imagine we have a population that is skewed to the right. If we were to take multiple samples from this population and calculate the mean of each sample, the distribution of those sample means would approach a normal distribution as the sample size increases. This is because the means tend to cluster around the true population mean, demonstrating the concept of the central limit theorem. In practical terms, the central limit theorem allows us to make assumptions about the population mean and standard deviation based on sample data, even if the population distribution is not normal. This is why the central limit theorem is considered one of the key foundations of statistical inference and is essential for interpreting research findings in various fields.