Hey guys this is my first blog. If there is sufficient demand I will continue to blog on various topics selected by you, the client. You run this blog. Ask me your questions. I would love to hear them.

For my first blog I will discuss the central limit theorem and why this is such an important concept in inferential statistics. Listen up. This will improve your grades.

The central limit theorem is a concept that allows us to make educated inferences about a population parameter from a sample. Why is this important? This is best explained using an example:

Say you are a student who is struggling with your statistics class and you need a very good tutor. Let's assume that you know from a previous study that the proportion of very good tutors on WyzAnt is only 12% This is obviously not a good scenario for you. You decide that you are going to take a sample of ten tutors and rate them as very good or not very good. From this sample you find that the proportion of very good tutors is 10% 1 out of 10 tutors in your sample received "very good" status. From this sample, and applying the central limit theorem, you can determine, within a margin of error, whether the true population of very good instructors is in fact 12% This is inferential statistics; and this is where I can help you. Don't settle for an average tutor; demand the best.

Look forward to working with you guys! Cheers!