This chapter has introduced the idea of using sample data to make statistical inferenceThe science of using a sample of measurements to make generalizations about the important aspects of a population.—that is, drawing conclusions about populations and processes by using sample data. We began by learning that a populationA set of existing units (people, objects, events, or the like) that we wish to study. is a set of existing units that we wish to study. We saw that, since many populations are too large to examine in their entirety, we often study a population by selecting a sampleA subset of the units in a population., which is a subset of the population units. Next we learned that, if the information contained in a sample is to accurately represent the population, then the sample should be randomly selected from the population, and we saw how random numbers (obtained from a random number tableA table containing random digits that is often used to select a random sample.) can be used to select a random sampleA sample selected so that, on each selection from the population, every unit remaining in the population on that selection has the same chance of being chosen. We also learned that selecting a random sample requires a frameA list of all of the units in a population. This is needed in order to select a random sample. (that is, a list of all of the population units) and that, since a frame does not always exist, we sometimes select a systematic sampleA sample taken by moving systematically through the population. For instance, we might randomly select one of the first 200 population units and then systematically sample every 200th population unit thereafter.

We continued this chapter by studying processesA sequence of operations that takes inputs and turns them into outputs.. We learned that to make statistical inferences about the population of all possible values of a variable that could be observed when using a process, the process must be in statistical control.A state in which a process does not exhibit any unusual variations. Often this means that the process displays a uniform amount of variation around a constant, or horizontal, level. We learned that a process is in statistical control if it does not exhibit any unusual process variations, and we demonstrated how we might sample a process and how to use a runs plot to try to judge whether a process is in control.

Next, in optional Section 1.4 we studied different types of quantitative and qualitative variables. We learned that there are two types of quantitative variable:A variable having values that are numbers representing quantities.— ratio variableA quantitative variable such that ratios of its values are meaningful and for which there is an inherently defined zero value., which are measured on a scale such that ratios of its values are meaningful and there is an inherently defined zero value, and interval variableA quantitative variable such that ratios of its values are not meaningful and for which there is not an inherently defined zero value., for which ratios are not meaningful and there is no inherently defined zero value. We also saw that there are two types of qualitative variablesA variable having values that are numbers representing quantities.— ordinal variablesA qualitative variable for which there is a meaningful ordering or ranking of the categories., for which there is a meaningful ordering of the categories, and nominative variableA qualitative variable for which there is no meaningful ordering, or ranking, of the categories., for which there is no meaningful ordering of the categories.

We concluded this chapter with optional Section 1.5, which discusses survey sampling. We introduced stratified random samplingA sampling design in which we divide a population into nonoverlapping subpopulations and then select a random sample from each subpopulation (stratum)., in which we divide a population into groups (strataThe subpopulations in a stratified sampling design.) and then select a random sample from each group. We also introduced multistage cluster samplingA sampling design in which we sequentially cluster population uints into subpopulations., which involves selecting a sample in stages, and we explained how to select a systematic sampleA sample taken by moving systematically through the population. For instance, we might randomly select one of the first 200 population units and then systematically sample every 200th population unit thereafter. Finally, we discussed some potential problems encountered when conducting a sample survey—undercoverageA situation in sampling in which some groups of population units are underrepresented., nonresponseA situation in which population units selected to participate in a survey do not respond to the survey instrument, response biasA situation in which survey participants do not respond truthfully to the survey questions., and slanted questions.