What is the central limit theorem?
The central limit theorem is the foundation of statistics. It states that if a sample is drawn from a population with large sample size, the distribution of the sample's mean will be distributed normally. In other words, the original population distribution will not be affected.
The central limit theorem is extremely useful in estimating confidence intervals and testing hypotheses. For instance, let's say I want to estimate the worldwide average height. I would take a sample of people from the general population and calculate the mean. Because it is difficult or impossible to collect data on every person's height, the mean of my sample will serve as my estimate.
To create a normal curve, we can plot the mean value and the frequency on a graph and then multiply them several times. The resulting curve will be similar to the original data set, but it will be slightly shifted to the left.
