Answer / Abhishek Srivatava

The Central Limit Theorem states that if you have a large number of independent and identically distributed (IID) random variables, their sum will tend to follow a Normal distribution. This is important in statistics as many data sets can be modeled as a sum of IID variables. A Normal Distribution is a continuous probability distribution characterized by bell-shaped curve. It is symmetrical about its mean (or average), and most of the observations fall around the mean. A Chi-Square Distribution, on the other hand, is a non-symmetrical distribution used in statistics to model the sum of squared standard normal variables. It is often used for testing independence or goodness of fit.

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