Chapter 10: Misconceptions

When people read, hear, or prepare research summaries, they sometimes have misconceptions about what is or isn't "sound practice" regarding the collection, analysis, and interpretation of data. Here are some of these common (and dangerous) misconceptions associated with the content of Chapter 10.

  1. In comparing the means of two groups, the null hypothesis must be set up to say that one population mean is equal to the other population mean.
  2. If the means of two groups are statistically compared and found to be significantly different, those two means must be quite different.
  3. A researcher who compares two means with an F-test is more sophisticated than the researcher who compares two means with a t-test.
  4. Correlated t-tests focuses on correlation coefficients.
  5. Two-tailed t-tests are used when 2 groups are compared whereas one-tailed tests are used in conjunction with one-sample t-tests.
  6. If two groups are compared with an analysis of variance, the researcher's primary interest is in the comparative degree of variability in each group.
  7. Two means that are significantly different at p < .001 must be further apart than two means that are significantly different at only p < .05.
  8. A test that has adequate power to detect large effects has even greater power to detect small effects.
  9. When interested in comparing the means of two samples that differ in size, smart researchers discard data from the larger group in order to make the sample sizes equal which then causes the t- or F-test to be robust.

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Schuyler W. Huck
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