AP Stat  Unit 5:  The Basis of Inference

Chapter 9:  Sampling Populations        Chapter 10:  Confidence Intervals & Hypothesis Testing

Chapter 9 lays the groundwork for the rest of the course.  The central idea is that if you sample a population many times for some variable, and compute a statistic (mean or proportion) of each sample, then the statistics you collected will be approximately normally distributed even if the underlying distribution of the population is not normal.  This idea is known as the Central Limit Theorem, and allows us to estimate the unknown mean and standard deviation or unknown proportion of the population by taking only one sample.

Chapter 10 contains the basic logic that is used in all statistical inference.  First we see how to form a confidence interval, which is a way of giving a range within which we believe a population parameter (like mean or proportion) lies.  We then see how to test the truth of some assumption about a population parameter by taking one sample and getting a sample proportion or mean, than asking what is the probability of getting those results IF the assumption is true.  If the probability is too low to believe, then we reject the assumption and accept an alternative hypothesis.  In this chapter we will learn how to form and test hypotheses.