Chapter 6 - Making Sense of Statistical Significance

Decision Errors

Incorrect conclusions in hypothesis testing in relation to the real (but unknown) situation, such as deciding the null hypothesis is false when it is really true.

Type 1 Error

Rejecting the null hypothesis when in fact it is true; getting a statistically significant result when in fact the research hypothesis is not true.

Alpha (α)

The probability of making a Type I error; same as significance level.

Type II error

Failing to reject the null hypothesis when in fact it is false; failing to get a statistically significant result when in fact the research hypothesis is true

Beta (β)

Probability of making a Type II error.

Effect Size

Standardized measure of difference (lack of overlap) between populations. Effect size increases with greater differences between means.

Effect Size Conventions

Standard rules about what to consider a small, medium, and large effect size, based on what is typical in psychology research; also known as Cohen’s conventions

Meta-Analysis

Statistical method for combining effect sizes from different studies.