The central limit theorem (CLT) states that the distribution of a random variable in a sample will begin to approach a normal distribution as the sample size becomes larger, regardless of the true shape of the distribution. Thus, we know that flipping a coin is a binary process, described by the binomial distribution (heads or tails). However, if we consider several coin tosses, the odds of getting any particular combination of heads and tails begin to differ. For instance, if we were to flip the coin ten times, the odds of getting 5 of each is most likely but getting ten heads in a row is extremely rare. Imagine 1,000 coin flips, and the distribution approaches the normal bell curve.