# Statistics

This chapter allows you to discuss a variety of
statistical issues. In addition to covering the independent groups *t* test (see Lecture 10.2,), you could talk about effect size (see box 10.2), the controversy about significance testing (to
give a balanced view of both sides of the debate, see this link for an elegant defense of significance testing
or see Appendix D), the relative
value of one-tailed versus two-tailed tests, or about nonparametric alternatives
to the traditional* t* test. Generally, you will want to cover the following
statistical issues:

### Null hypothesis

Emphasize the fact that one either rejects the null
hypothesis or fails to reject it: The null hypothesis is never supported. The
inability to find a difference doesn't mean that a difference doesn't exist.
This fact affects what hypotheses researchers can test (they can't prove that
something has no effect or that two manipulations have identical effects) and
how researchers interpret null results. (Table 10-2 will help you make these
points). You could even talk about the controversy about the null hypothesis. In discussing the controversy, you may wish to refer to the the home page for the Journal of Articles in Support of the Null Hypothesis and to APA's preliminary report on hypothesis testing.

### Type I and Type II errors

To help students understand the statistical
significance decision, what can go wrong (Type I and Type II errors), and the
tradeoffs made between Type I and Type II errors, review Table 10-3. Then, have students generate examples
of each kind of error and present them with different situations, asking them
which error would be more serious for that situation. To illustrate that these
errors do not occur solely in psychological investigations, you might ask
students what the implications are of the different errors for jury decision making, polygraph tests, pregnancy tests, AIDS testing of individuals, AIDS testing of donated blood, car alarms, drug tests (for example, according to one report, Type 2 errors in drug testing were 33%, Type 1 66.5%.).
In addition, going over Handout 10.6 is helpful to some students.

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