Final answer:
Interpreting p-values involves comparing them to an alpha level. A p-value less than alpha generally leads to rejecting the null hypothesis, indicating a significant effect. The actual conclusions depend on the specific p-values compared to the given alpha levels.
Step-by-step explanation:
The question concerns interpreting p-values with respect to a given alpha level (a), typically used in hypothesis testing to determine whether there is enough evidence to reject a null hypothesis. For both tests A and B, comparing the p-value to the alpha level will provide us with conclusions about the effects of height and weight on shoe size, respectively. If the p-value is less than or equal to the alpha level, we reject the null hypothesis, suggesting that there is a statistically significant effect.
- If the p-value is 0.05 and alpha is 0.05, we are at the threshold; typically, we still retain the null hypothesis unless the p-value is strictly less than alpha.
- If the p-value is 0.02 and alpha is 0.05, we reject the null hypothesis, indicating a significant effect.
- If the p-value is 0.01, regardless of whether the alpha is 0.05 or 0.01, the p-value is less than alpha and we reject the null hypothesis - indicating a significant effect.
Therefore, the conclusions for each test, given the alpha levels, would depend on the actual p-values obtained, which are not provided in the scenario.
However, typically, if a p-value is less than the alpha level (for example, p-value of 0.01 and alpha of 0.05), there is sufficient evidence to suggest that a significant effect exists.