Statistical Significance, Effect Size, and Power Analysis

Flashcards covering key concepts from Chapter 12 on Statistical Significance, Effect Size, and Power Analysis.

Null Hypothesis Significance Testing (NHST)

A statistical method to determine if the observed difference between groups is due to chance; it evaluates the probability (p value) of obtaining the observed data under the null hypothesis.

Type I Error

The error of rejecting the null hypothesis when it is actually true; also known as a false positive.

Type II Error

The error of failing to reject the null hypothesis when it is actually false; also known as a false negative.

Statistical Power

The probability that a statistical test will correctly reject a false null hypothesis; it is influenced by sample size, effect size, and significance level.

Effect Size (r)

A measure of the strength of the relationship between two variables; it indicates the magnitude of a treatment effect or correlation.

p-value

The probability of obtaining results at least as extreme as the observed results, under the assumption that the null hypothesis is true.

One-tailed p-value

A p-value that tests for the possibility of the relationship in one specified direction.

Two-tailed p-value

A p-value that tests for the possibility of the relationship in both directions, split between the two tails of the distribution.

Alpha Level (α)

The threshold for rejecting the null hypothesis, commonly set at 0.05; it defines the probability of making a Type I error.

Robustness of a Relationship

Refers to the consistency of the relationship between two variables across different samples or conditions.