Question 1. Effect size is a measure of:

A. the difference between individual members of a sample

B. the extent to which two populations overlap

C. the extent to which two populations do not overlap

D. the statistical significance of a research study

Question 2. Which of the following is NOT a correct statement about effect size of a study finding:

A. It provides much information about statistical significance.

B. It is a standardized measure of lack of overlap between populations.

C. It increases with greater differences between means.

D. It can be converted to a standardized effect size.

Question 3. According to Cohen's conventions, for research that compares means, a large effect size in which only about 53% of the populations of individuals overlap would be:

A..5

B..6

C..7

D..8

Question 4. Some IQ tests have a standard deviation of 16 points. If an experimental procedure produced an increase of 3.2 IQ points, the effect size would represent a __________ effect size.

A. small

B. medium

C. large

D. extra large

Question 5. A standard verbal memory test is known to have a standard deviation of 10 points. If an experimental procedure produced an increase of 8 points, the effect size would represent a __________ effect size.

A. small

B. medium

C. large

D. unable to determine without additional information

Question 6. In what way is effect size most comparable to a Z score?

A. It can range from ?1 to +1

B. It provides a direct indication of statistical significance

C. It provides a standard for comparison for results across studies, even studies using different measures

D. All of the above

Question 7. Cohen has proposed some effect-size conventions based on the effects observed in psychology research in general because:

A. researchers frequently need to decide whether the effect size that they have found allows them to reject the null hypothesis

B. it is usually difficult to know how big an effect to expect from a given experiment

C. Cohen originally developed the relevant scales

D. they are more accurate than figuring a minimum meaningful difference

Question 8. The effect size conventions proposed by Cohen are useful to researchers for:

A. predicting the value of the measured variable to use for the experimental condition

B. evaluating research results to determine if they are statistically significant

C. predicting the effect of a study on various populations

D. determining the power of a planned study

Question 9. A statistical method for combining effect sizes from different studies is known as:

A. combination analysis

B. comparison analysis

C. multivariate analysis

D. meta-analysis

Question 10. Reviews of a collection of studies on a particular topic that use meta-analyses represent an alternative to traditional __________ articles. These traditional articles describe and evaluate each study and then attempt to draw some overall conclusion.

A. general educational method

B. computer-assisted research

C. engagement goal setting

D. narrative literature review

Question 11. It is useful to understand statistical power for which of the following reasons?

A. Determining the number of participants to use in an experiment

B. Making sense of findings in research articles

C. Understanding the implications of a study that is not statistically significant

D. All of the above

Question 12. If statistical power for a given research study is .40, one can say that: "Assuming the researcher's prediction is correct, the researcher has a __________ chance of attaining statistically significant results."

A.20%

B.40%

C.45%

D.80%

Question 13. When a study has only a small chance of being significant even if the research hypothesis is true, the study is said to have:

A. low power

B. low probability

C. low market value

D. low sample size

Question 14. Standard power tables are useful for:

A. directly determining the power of an experiment

B. determining the predicted score (but not the variance) for the group exposed to the experimental manipulation

C. determining the predicted effect size of a proposed experiment

D. determining the probability of falsely accepting the research hypothesis

Question 15. Effect size is one of the two major factors that contribute to power. Another factor is:

A. the sample's standard deviation

B. the minimum meaningful difference

C. the sample size

D. the mean of the known population

Question 16. A researcher may not be able to change the effect size of a planned study to increase power. Another aspect of a planned study that the researcher can usually change to increase power is:

A. the sample size

B. the beta level

C. the population parameters

D. the sample mean

Question 17. In actual practice, the usual reason for determining power before conducting a study is to:

A. eliminate the possibility that a mistake may occur

B. ensure that regardless of whether the research hypothesis is true, the experiment will yield a significant result

C. determine the number of participants needed to have a reasonable chance of getting a significant result if the research hypothesis is true

D. recognize the likelihood that the experiment will need to be repeated

Question 18. What effect will using a one-tailed test over a two-tailed test have on power (presuming the true population difference is in the expected direction)?

A. it will increase power

B. it will have no effect on power

C. it will decrease power

D. power cannot be calculated if a one-tailed test is used

Question 19. Using a two-tailed test makes it __________ to get significance on any one tail. Thus, keeping everything else the same, power __________ with a two-tailed test than with a one-tailed test.

A. easier; more

B. harder; less

C. easier; less

D. harder; more

Question 20. If the research hypothesis is true, but the study has a low level of power:

A. there is a high probability that the study will have a significant result

B. the probability of getting a significant result is low

C. the null hypothesis will almost certainly be rejected

D. the significance level selected is probably too lenient (for example, .10 instead of .05)

Question 21. Practical significance is a combination of statistical significance and:

A. effect size

B. the level of measurement (whether it is equal interval or ordinal)

C. the population parameters

D. the amount over or under that level that the sample scored

Question 22. In statistics, we cannot state that the research hypothesis is ever definitely false. However, if one fails to reject the null hypothesis in a study with a high level of power, this allows us to:

A. suspect that the research hypothesis may still be true

B. conclude that the research hypothesis is most likely false

C. make no statements about the research hypothesis

D. reject the notion that the effect size has anything to do with statistical significance

Question 23. What is the most likely explanation for why a study with a very small effect size came out significant?

A. the study had a large sample size

B. the study had a large population standard deviation

C. the researcher used an insensitive hypothesis-testing procedure

D. the researcher used a two-tailed test

Question 24. When judging a study's results, there are two important questions. They are:

A. How large is the power and how competent are the researchers?

B. How stringent is the significance level and how small is the effect size?

C. Is the result statistically significant and is the effect size large enough for the results to be meaningful?

D. Is the study replicable and can we draw conclusions despite not having attained statistical significance?

Question 25. If the results of a study are not statistically significant and the sample size is large, then:

A. the result is very important

B. the result proves the null hypothesis

C. the research hypothesis is probably false

D. the result proves the research hypothesis