Quiz (Chapter 8)


Effect Size, Power, CIs, and Bonferroni

The Seven-Step Version of Hypothesis Testing

  1. (T/F) The last step of the 7-step version of hypothesis testing involves determining whether Ho is true.
  2. (T/F) A result that's statistically significant can be completely devoid of any practical significance.
  3. The "yardstick" that's used to measure practical significance is called
    1. actual significance
    2. credibility
    3. effect size
    4. importance
  4. Look at Excerpts 8.3, 8.4, 8.5, and 8.6. In these excerpts, what 4 letters were used to represent measures of effect size?
  5. (T/F) Effect size can be measured numerically.
  6. What three words do researchers use to describe the 3 different "sizes" of effect size?
  7. What exactly would it mean if a researcher stated that his/her statistical test had a power of .90?
  8. In a post hoc power analysis, is the effect size computed by the researcher or is it chosen by him/her?
  9. If Ho is not rejected but statistical power is quite high, would the chances of a Type II error be high or low?

The Nine-Step Version of Hypothesis Testing
  1. How many steps of the 6-step version of hypothesis testing appear in the 9-step version of hypothesis testing?
  2. (T/F)  In the 9-step version of hypothesis testing, the researcher specifies ES after analyzing the sample data.
  3. (T/F)  In the 9-step version of hypothesis testing, the sample data are analyzed before a power level is specified.
  4. Of the 2 kinds of effect size measures ("standardized" or "raw"), which one is better?
  5. (T/F) The needed sample size for a study cannot be determined unless both ES & power are specified first.
  6. In Excerpt 8.9, if the n had been 50, the power would have been ___ (higher/lower) than .8.
  7. In Excerpt 8.10, which kind of effect size was used, standardized or raw?
  8. There is general agreement that power should be no lower than ___ .
  9. (T/F) If a researcher reaches a fail-to-reject decision at the end of the 9-step version of hypothesis testing, a critic cannot legitimately argue that a Type II error was made because the test was too insensitive.

Hypothesis Testing Using Confidence Intervals
  1. (T/F)  When hypothesis testing is conducted via one or more confidence intervals, there's no Ho or Ha.
  2. If a 95% confidence interval is used when doing hypothesis testing, the level of significance = ___ .
  3. If a researcher has a sample with data on two variables, if r = +.40, and if a 95% confidence interval around r extends from +.20 to +.57, should Ho be rejected if Ho: r = 0 (and Ha: r  0)?
  4. (Yes/No) In Excerpt 8.14, is the number .80 indicating statistical power?
  5. (T/F) If, in Excerpt 8.15, 40% of the people in the supervised group had improved, the result might have been a nonsignificant difference between the two groups.

Adjusting for an Inflated Type I Error Rate
  1. (T/F)  A Type I error is made when a researcher fails-to-reject a null hypothesis that's really false.
  2. If you blindly select 1 card from a single well-shuffled deck of cards, the probability that your card will be a "club" is .25 (i.e., 1 out of 4).  Instead of doing that, suppose you blindly select a single card from each of 3 well-shuffled decks of cards.  Here, the probability that you'll end up with at least one club in your set of 3 cards is:
    1. .25
    2. less than .25
    3. more than .25
  3. Do the terms "heightened probability of Type I error" & "inflated Type I error risk" mean the same thing?
  4. Would the Bonferroni adjustment technique ever be used in a study involving a single null hypothesis?
  5. (T/F) In Excerpt 8.19, the p-level of .017 would have been .025 if there had been 2 comparisons (rather than 3 comparisons).
  6. When the Bonferroni adjustment technique is used, critical values become ____ (more/less) demanding.
  7. Researchers sometimes use the "_____ modification" rather than the Bonferroni adjustment technique.

A Few Cautions
  1. (T/F) The notion of an "effect size" (in the 7-step version of hypothesis testing) is exactly the same as the notion of an "effect size" (in the 9-step version of hypothesis testing).
  2. (T/F) The numerical criteria for "small," "medium," and "large" effects remain the same regardless of the statistical focus or the kind of ES that's computed.
  3. (T/F) The 6-step version of hypothesis testing fails completely to address the distinction between statistical significance and practical significance.
  4. (Yes/No) If 21 null hypotheses are tested, each at a=.05, and if Ho is true in each and every one of the 21 cases, would it be smart to bet that one or more Type I errors will be made among the 21 tests.

Two Questions that are Supposed to be a Bit Challenging
  1. If all bivariate correlations are computed among 5 variables, and if each r is tested to see of it is significantly different from 0, what Bonferroni-corrected alpha level should be used if the researcher desires to have a 5% chance of one or more Type I errors occurring?
  2. Suppose 2 researchers (Mary & Larry) each secretly pull a sample from the same giant population.  They both measure their subjects on the same 2 variables (X & Y), they both test Ho: r = 0, they both conduct a 2-tailed test, and they both set a= .05.  If Mary uses 5,000 subjects while Larry uses only 50, will Mary's Type I Error rate be "inflated" as compared to Larry's?


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