Classwork_01.pdf - PSY 3020 F19 – S Han[Classwork 01 Name Discrimination or Random Chance CLASS PART In the year Robert Martin turned 54 the Westvaco

# Classwork_01.pdf - PSY 3020 F19 – S Han[Classwork 01 Name...

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PSY 3020, F19 S. Han [Classwork 01] 1 Name: Discrimination or Random Chance? CLASS PART In the year Robert Martin turned 54, the Westvaco Corporation decided to downsize. They laid off more than half of the engineering department, including Robert Martin. Later that year, he sued Westvaco, claiming he had been laid off because of his age. A major piece of Martin’s case was based on a statistical analysis of the ages of the Westvaco employees. This case is not about age; it is about discrimination. It is about an employee fighting back against what he sees as unfair treatment. If you are fired from a job, maybe you weren’t very good at it or maybe you were just unlucky. On the other hand maybe it was because someone thought you were the “wrong” gender, or your skin was the “wrong” color, or you looked too young or too old. How do you know? While the use of statistics alone cannot prove discrimination, statistics can provide evidence by detecting patterns that are consistent with the practice of discrimination. Let’s look at one department of the Westvaco Co. They had 10 hourly workers. Their ages arranged from youngest to oldest were 25, 33, 35, 38, 48, 55, 55, 55, 56, 64. The three workers who were laid off were ages 55, 55, and 64. We can visualize these two distributions of people, those that were retained and those that were laid off. We can also summarize these distributions. For instance, we could “condense” the data into a single number called a “summary statistic.” One possible summar y statistic is the average, or mean, age of the three workers who lost their jobs. 55 + 55 + 64 3 = 58