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Type I error (alpha error)

Type I error (alpha error). Occurs when an experimenter thinks she/he has a significant result, but it is really due to chance Analogous to a “false positive” on a drug test. Risk of a Type I error is the same as the significance level, e.g., p < .05

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Type I error (alpha error)

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

  2. Type II error (beta error) • Occurs when a researcher fails to find a significant result when, in fact, there was something significant going on. • Analogous to a “false positive” on a drug test. • 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? • Solutions: increase sample size, use more sensitive precise measures, use replication

  3. Type I and Type II errors

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

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