Download Chapter 9: Normal Distribution and Sampling Distributions

Chapter 9: Normal Distribution and Sampling Distributions
Looking Ahead: What Is This Chapter About?
In previous chapters, you learned about a number of different kinds of distributions. Some distributions,
such as the sample distribution, describe data that have been observed. Other distributions, such as
probability and sampling distributions, describe data that might be observed if an experiment is performed.
They are hypothetical or theoretical in the sense that they do not represent the outcome of an actual
experiment. These distributions are used in inferential statistics as models of the results that a researcher
should expect if certain assumptions are tenable. For example, in the previous chapter the binomial
distribution was used to describe the possible outcomes of tossing a coin five times under the assumption
that the coin is fair. In this chapter, you will see how another important model, the normal distribution, is
used to describe the possible outcomes of an experiment. In addition, several important new statistics are
described: standard score, standard error, and test statistic.
After reading this chapter, you should know the following:
■ How to convert scores to standard scores (z scores)
■ How to use standard scores to find the size of areas under the normal distribution
■ Three characteristics of the sampling distribution of the mean
■ Two properties of good estimators
■ The difference between sample statistics and test statistics