Central Limit Theorem in action

You've seen how increasing the sample size changes both the statistics as well as the histogram. Now you will calculate the statistics of the entire population and observe the central limit theorem in action.

As a reminder, here's the definition:

If a sample size from an independent, random variable is large enough, then the sampling distribution will be normal or nearly normal.

"Large enough" is vague. The sample size is impacted by:

• How accurate you need to be. Since a sample is a representation, the resulting stats will be approximate. If you need a high degree of certainty, you will need more samples to more closely resemble the population.
• The more closely the population follows a normal distribution, the fewer sample points will be required.