Question

Are job applicants with easy to pronounce last names more likely to get called for an interview than applicants with difficult to pronounce last names. 500 job applications were sent out with last names that are easy to pronounce and 500 identical job applications were sent out with names that were difficult to pronounce. 138 of the "applicants" with easy to pronounce names were called for an interview while 104 of the "applicants" with difficult to pronounce names were called for an interview. What can be concluded at the 0.01 level of significance?

H0: PEasyToPronounce = PDifficultToPronounce

Ha: PEasyToPronounce [ Select ] ["<", ">", "Not Equal to"] PDifficultToPronounce

Test statistic: [ Select ] ["t", "Z", "Chi-square", "F"]

p-Value = [ Select ] ["0.006", "0.154", "0.204", "0.321"] Round your answer to three decimal places.

a. What can be concluded at the 0.01 level of significance?

Answer 1

A Z-test reveals that at the 0.01 significance level, there is statistical evidence to suggest a significant difference in call-back rates for interviews between applicants with easy-to-pronounce and difficult-to-pronounce last names.

Step-by-step explanation:

The correct answer is option "not equal to" for the alternative hypothesis (Ha), and the test statistic is "Z". For the p-value, choose "0.006" because we are looking to compare proportions and this p-value indicates a significant difference at the 0.01 level of significance. The correct hypothesis relationship is 'not equal to', with a p-value of 0.006, indicating the null hypothesis should be rejected.

With a p-value of 0.006, which is less than the alpha level of 0.01, we reject the null hypothesis (H0: PEasyToPronounce = PDifficultToPronounce). This means there is statistically significant evidence to suggest that job applicants with easy-to-pronounce names are more likely to be called for an interview than those with difficult-to-pronounce names.

Studies show that this kind of bias is not exclusive to names, as discrimination can also occur based on other factors such as ethnicity or foreign experience. For example, in Toronto, individuals with certain ethnic names or foreign experience were less likely to be hired than applicants with English names.