  Chapter 9: 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 9. When testing a single correlation, the null hypothesis must be set up to say that the correlation in the population is equal to 0.00. If a researcher asserts that "p < .001" when testing Ho: r = 0, you can be confident that the sample value of r was closer to +1.00 (or -1.00) than 0. The sampling distribution of r is symmetrical. If a researcher sets alpha equal to .05 when testing each of the correlation coefficients in a correlation matrix, he/she has a 1-in-20 chance of making a Type I error. It's important for researchers to test reliability and validity coefficients against a null hypothesis that says Ho: r = 0. If a correlation coefficient turns out to be statistically significant, there's no need to compute r2. Since the "error" associated with less-than-perfect measuring instruments is considered to be random (rather than systematic), this kind of error will "balance itself out" and not cause correlation coefficients to be systematically higher or lower than they ought to be. If the test of a correlation coefficient turns out to be statistically significant, this indicates that the mean on variable X is significantly different from the mean on variable Y. Tests of Pearson's r are robust to the underlying assumptions of linearity and homoscedasticity.