Question: Researchers must be careful to differentiate between resultant random and systematic errors. Furthermore, they must also realize that statistical precision does not necessarily validate results. Rather than focusing on statistical significance in and of itself, researchers need to identify results that have managerial significance-results that are relevant to the decision-making process. Given a large enough sample, any null hypothesis can be discounted, and any two means can be shown to be statistically different. An absence of statistical significance may be just as relevant as any demonstrated statistical significance. As such, statistical testing should be used as a tool to discover practical insights, not to define them.
1. Of the potential causes for error described above, which do you think would be easiest to identify? Hardest? Explain your reasoning.
2. Can you think of any ways that could help researchers determine whether occurrences of statistical significance in their results have managerial significance?