1. Why is significance an important construct in the study and use of inferential statistics?

2. Given the following information, would your decision be to reject or fail to reject the null hypothesis? Setting the level of significance at .05 for decision making, provide an explanation of your conclusion.

a. The null hypothesis that there is no relationship between the type of music a person listens to and his crime rate (p<.05)

b. The null hypothesis that there is no relationship between the amount of coffee consumption and GPA (p=.62)

c. The null hypothesis that there is a negative relationship between the number of hours worked and the level of job satisfaction (p=.51)

3. What is wrong with the following statements?

a. A Type I error of .05 means that 5 times out of 100, I will reject a true null hypothesis.

b. It is possible to set the Type I error rate to 0.

c. The smaller the Type I error rate, the better the results.

4. Why is it "harder" to find a significant outcome (all other things being equal) when the research hypothesis is being tested at the .01 rather than the .05 level of significance?

5. Why should we think in terms of "failing to reject" the null, rather than just accepting it?

6. Here's more on the significance-meaningfulness debate.
a. Provide an example where a finding may be statistically significant and meaningful.
b. Now provide an example where a finding may be statistically significant and not meaningful.

7. What does chance have to do with testing the research hypothesis for significance?