In this study, the null hypothesis was not rejected.

Here’s why:

• The test statistic is given as t(45) = 1.31, where 45 is the degrees of freedom (df).
• The p-value is reported as p > .05.
• The significance level (⍺) is .05.

Since the p-value is greater than the significance level (p > .05), there is not enough evidence to reject the null hypothesis. In hypothesis testing, when p > ⍺, it means the result is not statistically significant, and we fail to reject the null hypothesis.

Conclusion: The null hypothesis was not rejected.

Would you like more details on hypothesis testing or any related concepts?

Here are 5 related questions:

1. What does a p-value represent in hypothesis testing?
2. How do you calculate the degrees of freedom in a t-test?
3. What happens when the p-value is less than ⍺?
4. How is the t-statistic calculated in hypothesis testing?
5. What are Type I and Type II errors in hypothesis testing?

Tip: Always check both the p-value and significance level when interpreting the results of a hypothesis test.