1. Jump to first page Type I error (alpha error) n Occurs when an experimenter thinks she/he has a significant result, but it is really due to chance n Analogous to a “false positive” on a drug test. n Risk of a Type I error is the same as the significance level, e.g., p <.05 n Solutions: avoid internal validity errors (such as confounding variables), use a more stringent significance level, use replication

2. Jump to first page Type II error (beta error) n Occurs when a researcher fails to find a significant result when, in fact, there was something significant going on. n Analogous to a “false positive” on a drug test. n Must be calculated with a test of statistical “power,” e.g., given the sample size, how big would an effect have to be in order to detect it? n Solutions: increase sample size, use more sensitive precise measures, use replication

3. Jump to first page Type I and Type II errors

4. Jump to first page Implications n To some extant, Type I and Type II errors trade off with one another u Decreasing the chance of a Type I error may increase the chance of a Type II error. n A Type I error is the more egregious of the two u Type I entails shouting “Eureka” when you haven’t really found it. u Scientific skepticism makes Type II errors more palatable