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Using Statistics

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John and Sons Company has multiple production plants located throughout the United States and overseas. Right now it is pondering about opening a new manufacturing plant. However, it is not sure as to whether it wants to start this plant at home or abroad. There is a need for an appropriate null hypothesis to measure the quality of the product produced in the overseas plant and the one at home. The null hypothesis is given as under:

H0: The quality of the product produced at home and abroad is the same.

This is an appropriate null hypothesis as it will answer our question whether one production plant delivers a higher quality than the other, or whether the two have the same quality (Bowerman, O'Connell, & Murphree, 2010, pp. 347-9). The level of significance will be checked through the most common confidence level chosen for the hypothesis testing. Mostly, the hypotheses are checked at a confidence interval of 95%, which has a corresponding significance level of 5%. This is measured through a p-value of 0.05 or less (Bowerman, O'Connell, & Murphree, 2010, pp.  354-55).

This statistical test will only tell us whether the produced quality of the product in the two plants is the same or statistically different from each other. Then, we will not be able to know as to which plant has a superior quality product. For that, we will have to conduct a one-tailed test which will alter our hypothesis (Bowerman, O'Connell, & Murphree, 2010, pp. 354-6).

The everyday work problems can be solved through research. This research requires specifying research questions, setting hypotheses, collecting data, analyzing data, conducting hypothesis testing, and knowing the results through our findings. This will require a comprehensive knowledge of statistics, business research, and inferential statistics. Here, time-series forecasting and knowledge of the decision theory will also come handy (Bowerman, O'Connell, & Murphree, 2010, pp. 694, 830).

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