Hypothesis testing
In this week’s discussion post I will be presenting information to you as a marketing manager, working at a ready-to-eat breakfast cereal company. Our company recently initiated a loyalty program for our consumers and created a robust purchaser database. In this post I will be assessing how the appropriate use of hypothesis testing would validate cross-region purchases differences. I will explain the goal of said hypothesis testing experiment. I will describe the hypothesis testing process mechanics. And finally, I will explain why the organization would go through the hypothesis testing in this particular situation.
How does one assess how appropriate use of hypothesis testing would validate cross-region purchases differences? According to Lisa Sullivan, PhD, a professor of bio-statistics at Boston University School of Public Health, there are five steps involved during a hypothesis testing. They are as follows; step one is set up and select a level of significance, step two is choosing the right test statistic, step three is creating the decision rule, step four is computing the test statistic, and step five is to find a conclusion (Lisa Sullivan, 2022). When attempting to test a theory as to cross check opposite regions within the country for purchase differences, the correct hypothesis is necessary for a cost-effective budget and financial accounting, in order to ensure continue increased profit margins. This proposal needs to be airtight in its approach and following the above steps would ensure the most appropriate outcome.
Now to give an explanation of the goal of said hypothesis testing experiment. According to Roger B. Davis and Kenneth J. Mukamal of the Circulation Journal, the goal of a hypothesis testing experiment is to make a conclusion about a particular population based off the data gained from a sample of said population, in order to evaluate the strength of the evidence gathered, which provide a framework for making executive decisions related to that population (Davis & Mukamal, 2006). For this particular testing experiment my ready-to-eat breakfast company would like to design more effective promotional programs in order to reach a greater population throughout the country in all four regions. The main goal is to conduct a hypothesis experiment with the most level of significance with greater validity strength. This strength will ensure a firm direction in promotional cost savings.
Let me describe the hypothesis test process mechanics. According to Jason Walker, a consultant anesthetist at Ysbyty Gwynedd Hospital, to test a null hypothesis, you must gather information (sample of a population) and gauge its strength based off mathematical formulas. First, we must decide two independent population samples, creating the null hypothesis. Second, we must calculate the probability of the null hypothesis, either it is true or false. This probability would be known as a p-value, which is a number between 0 and 1. Zero would indicate impossible outcome, and one would indicate the hypothesis is certain to be true. This numerical outcome would determine the actual strength of the evidence. And Finally, depending on the strength of evidence, the person in charge of the experiment would be able to conclude if the null hypothesis is correct or false (Walker, 2019).
Why would the organization go through the hypothesis testing in this particular situation? Well, according to Tim Stobierski, a contributor of Analytics, Business Analytics, data-driven decision-making elements add several benefits to any organization or company, allowing an individual/s to seek out new positive opportunities and avoid possible threats (Stobierski, 2021). In Business, making decisions based off flawed thinking or observations lead to misused resources, unseen potential opportunities, and fatal outcomes. Hypothesis testing in business ensures a professional’s proposals and ideas are sound before implementing them into action. Hence ensuring the decision has a positive and profitable outcome measurement.
As you have read, I have assessed how the appropriate use of hypothesis testing would validate cross-region purchases differences. I have explained the goal of said hypothesis testing experiment. I have described the hypothesis testing process mechanics. And finally, I have explained why the organization would go through the hypothesis testing in this particular situation, in order to ensure my ready-to-eat breakfast company designed a more effective promotional programs in order to reach a greater population throughout the country in all four regions. All of this is to lead to higher profit margins with decrease overhead cost.
Thank you,
Resources:
Davis, R., & Mukamal, K. (2006, September 5). Hypothesis Testing. Retrieved from www.ahajournal.org: https://www.ahajournals.org/doi/10.1161/circulationaha.105.586461
Lisa Sullivan, P. (2022, March 10). Hypothesis Testing for Means and Proportions. Retrieved from www.sphweb.bumc.bu.edu: https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_hypothesistest-means-proportions/bs704_hypothesistest-means-proportions_print.html
Stobierski, T. (2021, March 30). A Beginner’s Guide to Hypothesis Testing in Business. Retrieved from Harvard Business School Online: https://online.hbs.edu/blog/post/hypothesis-testing
Walker, J. (2019, May 14). Hypothesis tests. Retrieved from www.ncbi.nim.nih: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807926/
Sample Solution
Hypothesis testing is a statistical procedure in which an analyst verifies a hypothesis about a population parameter. The analyst’s approach is determined by the type of the data and the purpose of the study. The use of sample data to assess the plausibility of a hypothesis is known as hypothesis testing. Such information could originate from a wider population or a data-gathering mechanism. In the following descriptions, the word “population” will be used to describe both of these scenarios. An analyst performs hypothesis testing on a statistical sample with the purpose of proving the null hypothesis’s plausibility.
