“Statistics” means “numerical descriptions” to most people. Monthly housing starts, the failure rate of liver transplants, and the proportion of African-Americans who feel brutalized by local police all represent statistical descriptions of large sets of data collected on some phenomenon. (Later, in Section 1.4, we learn that not all data is numerical in nature.) Often the data are selected from some larger set of data whose characteristics we wish to estimate. We call this selection process sampling. For example, you might collect the ages of a sample of customers who shop for a particular product online to estimate the average age of all customers who shop online for the product. Then you could use your estimate to target the Web site’s advertisements to the appropriate age group. Notice that statistics involves two different processes: (1) describing sets of data and (2) drawing conclusions (making estimates, decisions, predictions, etc.) about the sets of data on the basis of sampling. So, the applications of statistics can be divided into two broad areas: descriptive statistics and inferential statistics.

Although we’ll discuss both descriptive and inferential Statistics in the chapters that follow, the primary theme of the text is inference.

Let’s begin by examining some studies that illustrate applications of statistics.

Study 1.1 “Best-Selling Girl Scout Cookies”

Since 1917, the Girl Scouts of America have been selling boxes of cookies. Currently, there are 12 varieties for sale: Thin Mints, Samoas, Lemonades, Tagalongs, Do-si-dos, Trefoils, Savannah Smiles, Thanks-A-Lot, Dulce de Leche, Cranberry Citrus Crisps, Chocolate Chip, and Thank U Berry Much. Each of the approximately 150 million boxes of Girl Scout cookies sold each year is classified by variety. The results are summarized in Figure 1.1. From the graph, you can clearly see that the best-selling variety is Thin Mints (25%), followed by Samoas (19%) and Tagalongs (13%). Since the figure describes the various categories of boxes of Girl Scout cookies sold, the graphic is an example of descriptive statistics.

Study 1.2 “Are Action Video Game Players Better than Non-gamers at Complex, Divided Attention Tasks?”

Researchers at the Universities of Illinois (Urbana-Champaign) and Central Florida conducted a study to determine whether video game players are better than non-video game players at crossing the street when presented with distractions. Each in a sample of 60 college students was classified as a video game player or a non-gamer. Participants entered a street crossing simulator and were asked to cross a busy street at an unsigned intersection. The simulator was designed to have cars traveling at various high rates of speed in both directions. During the crossing, the students also performed a memory task as a distraction. The researchers found no differences in either the street crossing performance or memory task score of video game players and non-gamers. “These results,” say the researchers, “suggest that action video game players [and non-gamers] are equally susceptible to the costs of dividing attention in a complex task.” Thus, inferential statistics was applied to arrive at this conclusion.

Study 1.3 “Does Rudeness Really Matter in the Workplace?”

Previous studies have established that rudeness in the workplace can lead to retaliatory and counterproductive behavior. However, there has been little research on how rude behaviors influence a victim’s task performance. Consider a study where college students enrolled in a management course were randomly assigned to one of two experimental conditions: rudeness condition (45 students) and control group (53 students). Each student was asked to write down as many uses for a brick as possible in five minutes; this value (total number of uses) was used as a performance measure for each student. For those students in the rudeness condition, the facilitator displayed rudeness by berating the students in general for being irresponsible and unprofessional (due to a late-arriving confederate). No comments were made about the late-arriving confederate for students in the control group. As you might expect, the researchers discovered that the performance levels for students in the rudeness condition were generally lower than the performance levels for students in the control group; thus, they concluded that rudeness in the workplace negatively affects job performance. As in Study 1.2, this study is an example of the use of inferential statistics. The researchers used data collected on 98 college students in a simulated work environment to make an inference about the performance levels of all workers exposed to rudeness on the job.

These studies provide three real-life examples of the uses of statistics. Notice that each involves an analysis of data, either for the purpose of describing the data set (Study 1.1) or for making inferences about a data set (Studies 1.2 and 1.3).