# Wk 5 Discussion – Explaining Results – assignmentcollections.com

Business Finance Assignment Collections – assignmentcollections.com

Post a total of 3 substantive responses over 2 separate days for full participation. This includes your initial post and 2 replies to other students. You have two due dates for your responses. The first response is due by Thursday and the other responses are due by Monday. Note that you can always post more than 3 responses to the weekly discussion and your responses can be posted prior to the due dates.

Respond to the following in a minimum of 175 words:

Review Section 10.3 that covers Dependent Sample tests – these are frequently used in business operations to determine the effectiveness of new processes, changes to technologies and training of staff. The paired t test is used for the hypothesis test conducted using dependent samples. Review the example provided for the panel data in Figure 10.5 and post your thoughts on the application of the dependent sample tests in your business context.

An important part of using statistics is being able to explain your results to decision makers. Imagine that you have conducted a two-sample test and determined that the difference was not statistically significant. While one mean was 4.3 and the other was 3.9, the p level for the t test was p=.07. Your management team says, “Well, difference may not be statistically significant, but the difference is there! Discuss how you would respond and how you would explain the purpose of the t test and significance in this case.

Respond in 100 word count

By Toshamekia

The two-sample t-test produces a value p-level that describes the likelihood that there actually is a difference. The mean of each sample that was collected involves some randomness. For example, you could roll a dice 4 times and get three 6’s and a five; that doesn’t mean that dice has a 75% chance of yielding a 6 value.

In this case, there was a difference in the sample means (4.3 vs 3.9); the two sample t-test tells us is whether we should believe there actually is a difference between the two populations, or if the difference should just be chalked up to random probability. Given a p-value of 0.07, the difference is not statistically significant. This refers not to the size of the difference but instead confirms the fact that this difference is likely due to random probability/variance.

Simply put, statistical significance doesn’t have anything to do with the size of the difference; it indicates whether there actually exists a difference at all. In this case, there likely is not a difference.

Richard

The dependent sample t-test is a member of the t-test family. All tests from the t-test family compare one or more mean scores with each other. The t-test family is based on the t-distribution, sometimes also called Student’s t. Student is the pseudonym used by W. S. Gosset in 1908 to publish the t-distribution based on his empirical findings on the height and the length of the left middle finger of criminals in a local prison. Within the t-test family the dependent sample t-test compares the mean scores of one group in different measurements. It is also called the paired t-test, because measurements from one group must be paired with measurements from the other group. The dependent sample t-test is used when the observations or cases in one sample are linked with the cases in the other sample. This is typically the case when repeated measures are taken, or when analyzing similar units or comparable specimen.” (Statistics, 2019)

The t-test basically looks at two dissimilar sets of numbers and compare the mean scores of each. This is commonly utilized to make business decisions in continuous improvement or lean process projects. In my current role, we evaluate established and new business processes utilizing the t-test of sorts. We are trying to determine if it can help us determine the probability and / or measure effectiveness of the new or existing process to eliminate waste and increase efficiencies. We ask a few questions, will the process yield a sustainable and desirable results. We rely heavily on the trends illustrated by the data. These trends assist in us making a decisions on moving forward, implementing change, and even formulating a terminating action.

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