
…etween frequentist and Bayesian statistics is fundamental. The textbook example is flipping a coin. A frequentist statistician would flip the coin a million times, and, if 500k heads were observed, declare that the coin is fair. A Bayesian statistician would start with a prior belief on whether the coin is fair or not, and as he flips the coin, incrementally adjusts his belief based on the evidence.
Central limit theorem states that when we add large number of independent random variables, irrespective of the original distribution of these variables, their normalized sum tends towards a Gaussian distribution. For example, the distribution of total distance covered in an random walk tends towards a Gaussian …