There are a myriad of current and potential applications for two sample hypothesis tests within my organization. To begin with, our Human Resources Department utilizes two-tailed two sample hypothesis tests to ensure we remain compliant with Equal Employment Opportunity Commission (EEOC) and Title VII requirements by ensuring our pre-employment test is not unintentionally adversely impacting a group of applicants (Collins & Morris, 2008). Therefore, they utilize two-tailed two sample hypothesis tests to compare the mean test results of men and women, the proportion of men versus women that fail the test, and the proportion of male and female applicants that elect not to proceed or are not selected to proceed to the next step of the screening
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Therefore, we conduct two-tailed two sample hypothesis tests to compare device manufacturers, such as, Apple and Samsung and determine if there is a difference beyond the significance level in the proportion of their devices that must be replaced due to warranty related issues (Poncet et. al., 2016). Moreover, our Customer Service and Marketing departments also conduct two-tailed two sample hypothesis tests to assess the proportion of customers with an Apple or Samsung device that call customer service within 30 days of activation with questions about the device and determine statistically significant result beyond our significance level (Mirabella, 2011). Subsequently, we are able to accurately assess if there are any statistically valid variances in device quality and usability between device manufacturers. In conclusion, these are just a few of the many ways my organization utilizes two sample hypothesis testing to assess the statistical similarities and differences of various business processes (Barchard,
Therefore, we conduct two-tailed two sample hypothesis tests to compare device manufacturers, such as, Apple and Samsung and determine if there is a difference beyond the significance level in the proportion of their devices that must be replaced due to warranty related issues (Poncet et. al., 2016). Moreover, our Customer Service and Marketing departments also conduct two-tailed two sample hypothesis tests to assess the proportion of customers with an Apple or Samsung device that call customer service within 30 days of activation with questions about the device and determine statistically significant result beyond our significance level (Mirabella, 2011). Subsequently, we are able to accurately assess if there are any statistically valid variances in device quality and usability between device manufacturers. In conclusion, these are just a few of the many ways my organization utilizes two sample hypothesis testing to assess the statistical similarities and differences of various business processes (Barchard,