## Case study: gender discrimination (special topic)

We are building an openly accessible basis of evidence and lessons for working with boys and men to promote gender equality, by gathering, inter-relating, analysing and strategically disseminating evidence and lessons in targeted and accessible formats for improved learning, policy and practice. The case studies document learning from. When we conduct formal studies, usually we reject the notion that we just happened to observe a rare event. 51 So in this case, we reject the independence model in favor of the alternative. That is, we are concluding the data provide strong evidence of gender discrimination against . Jul 08, · Case Study Gender Discrimination - Free download as PDF File .pdf), Text File .txt) or read online for free. Scribd is the world's largest social reading and publishing site. Search Search. case study on sexual harassment of working women. Gender Discrimination in Job Market.5/5(1).

## Gender Discrimination Case Study | Case Study Template

Suppose your professor splits the students in class into two groups: students on the left and students on the right. While the proportions would probably be close to each other, *case study on gender discrimination* would be unusual for them to be exactly the same.

We would probably observe a small difference due to chance. If we don't think the side of the room a person sits on in class is related to whether the person owns an Apple product, what assumption are we making about the relationship between these two variables? We consider a study investigating gender discrimination in the s, **case study on gender discrimination**, which is set in the context of personnel decisions within a bank. Influence of sex role stereotypes on personnel decisions. Journal of Applied Psychology 59 1 The participants in this study are 48 male bank supervisors attending a management institute at the University of North Carolina in They were asked to assume the role of the personnel director of a bank and were given a personnel file to judge whether the person should be promoted to a branch manager position.

The files given to the participants were identical, except that half of them indicated the candidate was male and the other half indicated the candidate was female. These files were randomly assigned to the subjects. Is this an observational study or an experiment?

What implications does the study type have on what can be inferred from the results? Since this is an experiment, the results can be used to evaluate a causal relationship between gender of a candidate and the promotion decision.

For each supervisor we record the gender associated with the assigned file and the promotion decision. In this study, a smaller proportion of females are promoted than males *case study on gender discrimination.* Statisticians are sometimes called upon to evaluate the strength of evidence. When looking at the rates of promotion for males and females in this study, what comes to mind as we try to determine whether the data show convincing evidence of a real difference?

The observed promotion rates However, we cannot be sure if the observed difference represents discrimination or is just from random chance. Generally there is a little bit of fluctuation in sample data, and we wouldn't expect the sample proportions to be exactly equal, even if the truth was that the promotion decisions were independent of gender.

Independence model. The variables gender and decision are independent. They have no relationship, and the observed difference between the proportion of males and females who were promoted, Alternative model.

The variables gender and decision are not **case study on gender discrimination.** The difference in promotion rates of What would it mean if the independence model, which says the variables gender and decision are unrelated, is true? It would mean each banker was going to decide whether to promote the candidate without regard to the gender indicated on the file. That is, the difference in the promotion percentages was due to the way the files were randomly divided to the bankers, and the randomization just happened to give rise to a relatively large difference of Consider the alternative model: bankers were influenced by which gender was listed on the personnel file.

If this was true, and especially if this influence was substantial, we would expect to see some difference in the promotion rates of male and female candidates.

If this gender bias was against females, we would expect a smaller fraction of promotion decisions for female personnel files relative to the male files. Now, suppose the bankers' decisions were independent of gender. Then, if we conducted the experiment again with a different random arrangement of files, differences in promotion rates would be based only on random fluctuation. We can actually perform this randomizationwhich simulates what would have happened if the bankers' decisions had been independent of gender but we had distributed the files differently.

We will **case study on gender discrimination** 35 files into the first stack, which will represent the 35 supervisors who recommended promotion. The second stack will have 13 files, and it will represent the 13 supervisors who recommended against promotion, **case study on gender discrimination**.

The randomization of files in this simulation is independent of the promotion decisions, which means any difference in the two fractions is entirely due to chance. How does this compare to the observed This difference due to chance is much smaller than the difference observed in the actual groups. While in this first simulation, we physically dealt out files, it is more efficient to perform this simulation using a computer.

Repeating the simulation on a computer, we get another difference due to chance: And another: 0. And so on until we repeat the simulation enough times that we have a good idea of what represents the distribution of differences from chance alone. Note that the distribution of these simulated differences is centered around 0, *case study on gender discrimination*.

We simulated these differences assuming that the independence model was true, and under this condition, we expect the difference to be zero with some random fluctuation.

We would generally be surprised to see a difference of exactly *case study on gender discrimination* sometimes, just by chance, the difference is higher than 0, and other times it is lower than zero. How often would you observe a difference of at least Often, *case study on gender discrimination*, sometimes, rarely, or never? It appears that a difference of at least Such a low probability indicates a rare event. The difference of Gender has no effect on promotion decision, and we observed a difference that would only happen rarely.

Gender has an effect on promotion decision, and what we observed was actually due to equally qualified women being discriminated against in promotion decisions, which explains the large difference of Based on the simulations, we have two options. That is, we do not have sufficiently strong evidence to conclude there was gender discrimination. When we conduct formal studies, usually we reject the notion that we just happened to observe a rare event.

Each of us observes incredibly rare events every day, events we could not possibly hope to predict. However, in the non-rigorous setting of anecdotal evidence, almost anything may appear to be a rare event, so the idea of looking for rare events in day-to-day activities is treacherous, **case study on gender discrimination**.

For example, we might look at the lottery: there was only a 1 in million chance that the Mega Millions numbers for the largest jackpot in history March 30, would be 2, 4, 23, 38, 46 with a Mega ball of 23but nonetheless those numbers came up! However, no matter what numbers had turned up, **case study on gender discrimination**, they would have had the same incredibly rare odds.

That **case study on gender discrimination,** any set of numbers we could have observed would ultimately be incredibly rare. This type of situation is typical of our daily lives: each possible event in itself seems incredibly rare, but if we consider every alternative, those outcomes are also incredibly rare, *case study on gender discrimination*.

We should be cautious not to misinterpret such anecdotal evidence. So in this case, we reject the independence model in favor of the alternative. That is, we are concluding the data provide strong evidence of gender discrimination against women by the supervisors. One field of statistics, statistical inference, is built on evaluating whether such differences are due to chance.

In statistical inference, statisticians evaluate which model is most reasonable given the data. Errors do occur, just like rare events, and we might choose the wrong model, *case study on gender discrimination*.

While we do not always choose correctly, statistical inference gives us tools to control and evaluate how often these errors occur. We spend the next two chapters building a foundation of probability and theory necessary to make that discussion rigorous. Front Matter Preface Textbook overview Videos Examples, exercises, and appendices OpenIntro, online resources, *case study on gender discrimination*, and getting **case study on gender discrimination** Acknowledgements 1 Data collection Case study: using stents to prevent strokes Data basics Overview of data collection principles Observational studies and sampling strategies Experiments Exercises 2 Summarizing data Examining numerical data Numerical summaries and box plots Considering categorical data Case study: gender discrimination special topic Exercises 3 Probability Defining probability Conditional probability The Binomial Formula Simulations Random variables Continuous distributions Exercises 4 Distributions of random variables Normal distribution Sampling distribution of a sample mean Geometric distribution Binomial distribution Sampling distribution of a sample proportion Exercises 5 Foundation for inference Estimating unknown parameters Confidence intervals Introducing hypothesis testing Does it make sense?

Section 2. Figure 2. Solution The observed promotion rates Two of the simulations had a difference of at least Solution It appears that a difference of at least

### Gender Discrimination: A case study

(1) We conclude that the study results do not provide strong evidence against the independence model. That is, we do not have sufficiently strong evidence to conclude there was gender discrimination. (2) We conclude the evidence is sufficiently strong to reject \(H_0\) and assert that there was gender discrimination. Regardless, the scope of federal gender discrimination protections with respect to LGBT individuals is unsettled and remains a contentious issue. Below is a list of U.S. Supreme Court cases involving gender discrimination and women's rights, including links to the full text of the U.S. Supreme Court decisions. Jul 08, · Case Study Gender Discrimination - Free download as PDF File .pdf), Text File .txt) or read online for free. Scribd is the world's largest social reading and publishing site. Search Search. case study on sexual harassment of working women. Gender Discrimination in Job Market.5/5(1).