A researcher is interested in the effect of gender bias in hiring. A total of 100 scientists were given a fake job application and had to decide how likely they were to hire the candidate. 50 scientists received an application with a typically male name on top, while the other 50 scientists received an application with a typically female name on top. The applications were identical other than the name. The dependent variable was the scientists' rating of how likely they were to hire the candidate. Which hypothesis test would be appropriate to test the difference in rating between the male-name and female-name conditions?

The hypothesis test appropriate to be used here is t-test for the difference between two means.

Step-by-step explanation:

In this case, the researcher is interested in the effect of gender bias in hiring, i.e. the researcher wants to determine whether there is any difference in hiring between the male and female candidates.

So the researcher selects 100 fake job application; 50 scientists received an application with a typically male name on top, while the other 50 scientists received an application with a typically female name on top.

The scientists have to rate how likely they were to hire the candidate.

It is provided that the applications were identical other than the name. That is, there are no other factors affecting the hiring of the candidate other than their gender.

After rating the candidates the researcher can compute the mean rating for both the groups.

To determine whether there is any difference between the two means, the researcher can use the t-test for the difference between two means to draw conclusions about the experiment.

Thus, the hypothesis test appropriate to be used here is t-test for the difference between two means.

its a straight line parallel to the time axis

if you are saying purchasing 3 social media ads and one newspaper ad counts as one purchase then there are:

6 purchases

if you are asking how many of each then:

18 social media ads

6 newspaper ads