STT 212 Lecture Notes - Lecture 12: Central Limit Theorem, Sampling Distribution

• School: University of Rochester
• Department: Statistics
• Course: STT 212
• Professor: Mcdermott

Document Summary

The central limit theorem in statistics states that, given a sufficiently large sample size, the sampling distribution of the mean for a variable will approximate a normal distribution regardless of that variable"s distribution in the population . Unpacking the meaning from that complex definition can be difficult. I"ll walk you through the various aspects of the central limit theorem (clt) definition, and show you why it is so important in the field of statistics. Part of the definition for the central limit theorem states, regardless of the variable"s distribution in the population. this part is easy! In a population, values of a variable can follow different probability distributions. These distributions can range from normal, left skewed , right skewed , and uniform among others. This part of the definition refers to the distribution of the variable"s values in the population from which you draw a random sample .