C H A P T E R 16 Test driving – sampling theory, estimation and hypothesis testing Chapter objectives This chapter will help you to: ■ ■ ■ ■ ■ ■ ■ understand the theory behind the use of sample results for prediction make use of the t distribution and appreciate its importance construct and interpret interval estimates of population means and population proportions work out necessary sample sizes for interval estimation carry out tests of hypotheses about population means, proportions and medians, and draw appropriate conclusions from them use the technology; the t distribution, estimation and hypothesis testing in EXCEL, MINITAB and SPSS become acquainted with. | CHAPTER 16 Test driving - sampling theory estimation and hypothesis testing Chapter objectives This chapter will help you to understand the theory behind the use of sample results for prediction make use of the t distribution and appreciate its importance construct and interpret interval estimates of population means and population proportions work out necessary sample sizes for interval estimation carry out tests of hypotheses about population means proportions and medians and draw appropriate conclusions from them use the technology the t distribution estimation and hypothesis testing in EXCEL MINITAB and SPSS become acquainted with the business origins of the t distribution In the previous chapter we reviewed the methods that can be used to select samples from populations in order to gain some understanding of those populations. In this chapter we will consider how sample results can be used to provide estimates of key features or parameters of the Chapter 16 Test driving - sampling theory estimation and hypothesis testing 483 populations from which they were selected. It is important to note that the techniques described in this chapter and the theory on which they are based should only be used with results of samples selected using probabilistic or random sampling methods. The techniques are based on knowing or at least having a reliable estimate of the sampling error and this is not possible with non-random sampling methods. In Chapter 13 we looked at the normal distribution an important statistical distribution that enables you to investigate the very many continuous variables that occur in business and many other fields whose values are distributed in a normal pattern. What makes the normal distribution especially important is that it enables us to anticipate how sample results vary. This is because many sampling distributions have a normal pattern. Sampling distributions Sampling distributions are distributions that show how sample results vary. They
