Final answer:
In experiments with multiple groups, we should correct the p-value for the number of comparisons. For matched or paired sample hypothesis tests, true statements include that two measurements come from the same pair and two means are compared. Decisions are made by comparing the p-value to a predetermined alpha value.
Step-by-step explanation:
In an experiment with many groups, we should not consider a p-value without correcting it for the number of comparisons made. When multiple comparisons are made, there is a greater chance of obtaining at least one statistically significant result simply by chance. This is known as the multiple comparisons problem or the look-elsewhere effect. A common approach to addressing this issue is to apply adjustments such as the Bonferroni correction to control for the family-wise error rate
The correct answer is: D. Answer choices b and c are both true. In matched or paired samples, two measurements are drawn from the same pair of individuals or objects, and two sample means are compared to each other.
In the Decision and Conclusion phase of hypothesis testing, to decide whether to reject or not reject the null hypothesis, you compare the p-value with a preset alpha (α), which represents the level of significance and the probability of a Type I error. If alpha is greater than the p-value, you reject the null hypothesis, indicating that the findings are statistically significant. If alpha is less than or equal to the p-value, you do not reject the null hypothesis.