Type I and Type II Error Rates

There is a trade-off between Type I and Type II error rates. The Type I error rate (⍺) is set a priori (in advance) of the start of the hypothesis test. A researcher who is worried about making a Type I error could help minimize the chance of this mistake occurring by setting alpha very low, which increases the difficulty of rejecting the null hypothesis. The flipside, however, is that making it harder to reject a true null also makes it hard to reject a false one. By reducing the chance of a Type I error, the researcher has increased the risk of making a Type II error.

The choice between the different tests is made on the basis of each variable’s level of measurement. You must identify the levels of measurement of the IVs and DVs and then select the proper test for those measurement types. This book covers four types of bivariate inferential tests. The first is chi-square, which is the statistical procedure used to test for an association between two categorical (nominal or ordinal) variables. If, for example, you had a sample of criminal defendants and wanted to find out whether there was a relationship between the type of crime a defendant was charged with (violent or property) and the disposition method that the defendant chose (guilty plea, jury trial, or bench trial), you would use a chi-square test.

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