The Central Limit Theorem (CLT) is a fundamental concept in statistics that states that the distribution of the sample means will tend to be normally distributed, regardless of the original population distribution, as long as the sample size is sufficiently large. This theorem is crucial for making inferences about population parameters based on sample statistics.

In practical terms, the CLT allows data scientists to use sample data to make predictions and decisions about a larger population. For example, if you want to estimate the average blood sugar level of a large population, you can take a sample and calculate the mean. As the sample size increases, the distribution of the sample mean will approach a normal distribution, making it easier to apply statistical methods and hypothesis testing ¹³⁵.

The CLT is particularly important in data science, as it underpins many statistical techniques and methodologies used for analyzing data and drawing conclusions from it ²⁴. Understanding the CLT is essential for anyone working with data, as it provides a foundation for inferential statistics.