Monday, November 2, 2009

Katie,
you had a really good way and understandable way of explaining the Central Tendency Theorem. . . could you repeat it for the blog?

What does the Central Tendency Theorem mean?

christie

1 comment:

KGL said...

I hope this does the Central Limit Theorem justice (feel free to add or subtract what seems out of place others!)::

To put it in a simple version...as you increase the amount of SAMPLE distribution statistics, the closer you will come to the true POPULATION parameters.

So (as shown in lecture 7)...if we were to take a sample (n = 10) from our population, we would get statistics that are only estimates of the true population parameters, thus creating a sampling distribution. However, the more samples of 10 we get from the population, the more sampling distributions we create, and the closer we come to the actual true population parameters. These end up looking closer and closer to a normal distribution, the more sampling distributions you have.

So as you increase sample size... the mean, slope, intercept, etc. of your sampling distributions approach true population mean, slope, intercept, etc. which looks like a normal distribution.

Hope that helps. Ask away, it's harder to explain in text that in person...perhaps that's why Christie has such confidence in my explanation. =)