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 populations based on sample data, especially in the field of data science.

In practical terms, if you take random samples from a population and calculate their means, the distribution of those means will approximate a normal distribution as the sample size increases. This property allows data scientists to apply statistical methods that assume normality, even when the underlying data does not follow a normal distribution.

The CLT is particularly useful in real-world applications, such as estimating population parameters and conducting hypothesis testing. It serves as a cornerstone for many statistical techniques and is essential for anyone working in data science or related fields ¹²³⁵.