Hypothesis testing is used in business to test assumptions and theories. These assumptions are tested against the 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 complaint alleging that the bags are underfilled. A production manager randomly samples a batch and measures the following weights: Weight, (lbs.) [This is a single dataset presented in two columns] 45.6     49.5 47.7     46.7 47.6     48.8 50.5     48.6 50.2     51.5 46.9     50.2 47.8     49.9 49.3     49.8 53.1     49.3 49.5     50.1 To determine whether the bags are indeed being underfilled by the machinery, the manager must conduct a test of mean with a significance level ? = 0.05. In a minimum of 175 words, respond to the following in Blackboard: State appropriate null (Ho) and alternative (H1) hypotheses. What is the critical value if we work with a significant level ? = 0.05? What is the decision rule? Calculate the test statistic. Are the bags indeed being underfilled? Should machinery be recalibrated?