The central limit theorem is a fundamental concept in probability theory and statistics. It states that, under certain conditions, the distribution of the sum (or average) of a large number of independent and identically distributed random variables tends towards a normal distribution, regardless of the shape of the original distribution. In simpler terms, it means that when we add up or average a large number of random samples, the resulting distribution will become approximately bell-shaped, resembling a normal distribution. This has important implications in statistical inference and allows us to make reliable predictions and estimations based on sample data.