Hypothesis testing is used in business to test assumptions and theories. These assumptions are tested against evidence provided by actual, observed data. A statistical hypothesis is a statement about the value of a population parameter that we are interested in. Hypothesis testing is a process followed to arrive at a decision between 2 competing, mutually exclusive, collective exhaustive statements about the parameter’s value.
Consider the following scenario: An industrial seller of grass seeds packages its product in 50-pound bags. A customer has recently filed a complained alleging that the bags are underfilled. A production manager randomly samples a batch and measures the following weights:
To determine whether the bags are indeed being underfilled by the machinery, the manager must conduct a test of mean with a significance level α = 0.05.
In a minimum of 175 words, respond to the following:
- State appropriate null (Ho) and alternative (H1) hypotheses.
- What is the critical value if we work with a significant level α = 0.05?
- What is the decision rule?
- Calculate the test statistic.
- Are the bags indeed being underfilled?
- Should machinery be recalibrated?
Reply to at least 2 of your classmates or your faculty member or address any of the following subjects:
- Explain the difference between the Null and Alternate Hypothesis.
- Explain the difference between a One- and a Two-tail test.
- How do we determine the critical value and why is it important?
- What is the test statistic? How is it related to the area of significance?
Be constructive and professional in your responses.