Let's imagine a research study at a workplace where the HR department wants to investigate if a new training program improves employee productivity. Here's how we can frame the hypotheses and understand type I and type II errors: Research Question: Does the new training program improve employee productivity? Null Hypothesis (H0): The new training program does not affect employee productivity; any observed differences in productivity are due to random chance or other factors. Alternative Hypothesis (Ha): The new training program does improve employee productivity; there is a statistically significant increase in productivity among employees who undergo the training. Type I Error: A Type I error would occur if the study rejects the null hypothesis when it is actually true. In this context, it would mean concluding that the new training program improves productivity when, in fact, it does not. This could lead to unnecessary implementation costs or changes in policy based on false evidence of effectiveness. Type II Error: A Type II error would occur if the study fails to reject the null hypothesis when the alternative hypothesis is true. Here, it would mean failing to detect a real improvement in productivity due to the new training program. This could result in missed opportunities to enhance employee performance and potentially improve organizational outcomes. Type of Test: In this scenario, typically, we would consider this a one-tailed test. The reason is that we are sp