BUS308 – Week 1 Lecture 2 Describing Data Expected Outcomes After reading this lecture, the student should be familiar with: 1. Basic descriptive statistics for data location 2. Basic descriptive statistics for data consistency 3. Basic descriptive statistics for data position 4. Basic approaches for describing likelihood 5. Difference between descriptive and inferential statistics What this lecture covers This lecture focuses on describing data and how these descriptions can be used in an analysis. It also introduces and defines some specific descriptive statistical tools and results. Even if we never become a data detective or do statistical tests, we will be exposed and bombarded with statistics and statistical outcomes. We need to understand what they are telling us and how they help uncover what the data means on the “crime,” AKA research question/issue. How we obtain these results will be covered in lecture 1-3. Detecting In our favorite detective shows, starting out always seems difficult. They have a crime, but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as we are at this point with our question on equal pay for equal work. The process followed is remarkably similar across the different shows. First, a case or situation presents itself. The heroes start by understanding the background of the situation and those involved. They move on to collecting clues and following hints, some of which do not pan out to be helpful. They then start to build relationships between and among clues and facts, tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads, etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved. Data analysis, and specifically statistical analysis, is done quite the same way as we will see. Descriptive Statistics Week 1 Clues We are interested in whether or not males and females are paid the same for doing equal work. So, how do we go about answering this question? The “victim” in this question could be considered the difference in pay between males and females, specifically when they are doing equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining basic information to see if we even have cause to worry. The first action in any analysis involves collecting the data. This generally involves conducting a random sample from the population of employees so that we have a manageable data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and 25 females spread throughout the company. A quick look at the sample by HR provided us with assurance that the group looked representative of the company workforce we are concerned with as a whole. Now we can confidently collect clues to see if we should be concerned or not. As with any detective, the first issue is to understand the.