Describe a situation in which you would test a directional hypothesis. Be sure to state the independent variables (e.g., drug or placebo) and the dependent variables (e.g., weight loss) clearly and explain why the hypothesis is directional. Then, revise the same situation to make it non-directional. Explain which according to you is more appropriate and why. Evaluate the practice of altering the alpha level so that a two-tailed test will have a 5% rejection region on both sides of the curve for a total of 10% instead of having a 2.5% rejection region on both sides in order to maintain a 5% alpha.
A situation in which you might test a directional hypothesis is studying the effects of a new weight loss drug compared to a placebo on weight loss. In this scenario, the independent variable is the type of treatment given (drug or placebo), and the dependent variable is weight loss. The directional hypothesis would state that the weight loss drug will lead to greater weight loss than the placebo.
To make this situation non-directional, we can revise it to study whether the type of treatment (drug or placebo) has any effect on weight loss. The hypothesis then becomes non-directional, stating that there will be a difference in weight loss between the drug and placebo groups, without specifying the direction of that difference.
Determining which type of hypothesis is more appropriate depends on the specific research question and prior knowledge. If previous studies or theories suggest that the weight loss drug is expected to be more effective than the placebo, a directional hypothesis is appropriate. On the other hand, if there is no prior expectation or information to suggest the superiority of one treatment over the other, a non-directional hypothesis may be more appropriate.
Regarding the practice of altering the alpha level to maintain a 5% significance level in a two-tailed test, it is generally not recommended. The alpha level (often set at 0.05) represents the probability of making a Type I error, which is rejecting the null hypothesis (H0) when it is actually true. By dividing the alpha level equally between the two tails, you effectively increase the overall probability of making a Type I error.
In a standard two-tailed test, a 5% alpha level is evenly split into 2.5% rejection regions on both sides of the curve. This maintains a 5% overall significance level, allowing for a balanced evaluation of both sides of the hypothesis. Altering this to create a 10% overall rejection region with 5% on each side increases the likelihood of making a Type I error, as you would be more likely to reject the null hypothesis based on random chance alone.
Therefore, it is generally more appropriate to maintain a 2.5% rejection region on each side of the curve in order to maintain a 5% alpha level, ensuring a balanced and statistically sound evaluation of the hypothesis.