CLT and Learning

The Central Limit Theorem (CLT) is a statistical principle stating that the average of a large number of independent, random samples will resemble a normal distribution, regardless of the original distribution of the data. This means that even if the individual data points are varied or skewed, their average will tend to be predictable and follow a bell curve as more samples are taken. In terms of learning, this concept helps us understand how we gather knowledge: as we accumulate experiences and information, our understanding becomes more stable and reliable, resembling the predictable patterns seen in the CLT.