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Hypothesis testing is defined as “a procedure based on sample evidence and probability theory, to determine whether the hypothesis is a reasonable statement” (Marchal and Wathens, 2017). There are six (6) steps in the hypothesis testing, the first is stating the null and alternative hypotheses. The null hypotheses define the study or research and the alternate hypotheses is used if the null hypotheses prove to be untrue or fail. The second is to select a level of significance, the third step is to identify the test statistic, in the fourth step you formulate a decision rule, the fifth step, you take a sample and arrive at a decision in the sixth step and in the final step the result are  interpreted (Marchal and Wathens, 2017).

Based on the steps and analysis of hypothesis testing, the strengths are: save time in research methods, as the e.g. presented in the text on commission paid to hhgregg employees (Marchal and Wathens, 2017, pg. 320), it would be rather time consuming and costly to conduct interview in all stores, therefore another strength is cost savings to organizations that are look. Hypothesis testing is also used a lot in financial companies. A weakness is that since the procedure is based on sampling, averaging and statistical analysis, there are small likelihood of inaccuracies. For e.g. from the quote from Daniel, J, etal, “Be careful of averages and how they’re applied. One way that they can fool you is if the average combines samples from disparate populations. This can lead to absurd observations such as: ‘On average, humans have one testicle.’” (2016). That would be a huge error in the data and should be a rejection in hypothesis testing. Overall the strengths surplus the weakness because hypothesis testing has been widely used by scientist and for the most part has proven to be successful and has been meeting the purposes.

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Marchal and Wathens, (2017) Retrieved from

Daniel J. Levitin, A Field Guide to Lies: Critical Thinking in the Information Age (2016) Retrieved from:

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Upon reading the assignment and hearing the word hypothesis testing, I first thought of what the word hypothesis meant in science. I learned many years ago that the word hypothesis means an educated guess on what you perceive will happen before any research is done. I know I probably sound like a broken record, but going back to my UPS days, I used this data every day, but I did not know this was what it was called. We would have a historic day that would show delivery by stops and by volume. Stops were essential to the dispatch team as you did not want a driver making more than 110 stop if possible. Stops are referred to how many individual places the driver had to drive physically to make a delivery. For me, volume was vital because it showed a trend of years and gave a somewhat of an insight as to what we would be physically touching.

From a positive side, when this information was correct, it allows me to staff the facility and ensure that we have every truck loaded and ready for the drivers upon their arrivals. Also, this would help me to ensure that we ran our overall production goal for the day, which I believe was 89%. Most people would say that running 89% is easy, but the truth be told that the building was totally manual on my shift, and all it would take was one belt jamming which would lead to slowing the process down to ensure the loaders could handle the volume coming to them, also meaning you would have to stop the unload team hence getting them behind on their downtime.

But on the other hand, when this information was inaccurate if the volume was higher than forecasted it would mean that we would go down later than planned — saying that drivers would not depart the building in time, which could lead to service failures. UPS thrives off its next day air service; for those who have never shipped NDA, a simple letter from MS to TN would cost about $80-$90 and when there is a service failure the company is liable to give you a refund on the shipping cost. At the same time, if the volume was lower than forecasted, it meant that people would be standing around as we had to wait on the air trailer from the airport. This would cost me hitting my productivity goal.

I can only imagine what happens when hypothesis testing misses in the real world, such as in the sales forecast or the future unemployment rate.


Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics (17th ed.). Retrieved from

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