Demystifying Sampling Distributions and the Central Limit Theorem
IN THIS CHAPTER
Identifying a sampling distribution
Interpreting the central limit theorem
Using the central limit theorem to find probabilities
Knowing when you can use the central limit theorem and when you can’t
Many instructors love to talk on and on about the glories of the central limit theorem (CLT) and how important sampling distributions are to their being, but instructors should all face the fact that you probably don’t care about these ideas that much. You just want to get through your class, right? And of course, sampling distributions and any topics related to a “theorem” aren’t the easiest subjects on the statistics syllabus (most statistics teaching circles consider them to be the hardest and the most important — what luck). Before you decide to pack it in and call it quits, know that I feel your pain, and I’m here to help.
In this chapter, more than in any other, I think of you as being on a “need-to-know-only” basis. No extra stuff, no frilly theoretical gibberish, no talking about the CLT ...