PSYC 212 - Chapter 13: Inferential Statistics

Null Hypothesis Testing

A formal approach to deciding between two interpretations of a statistical relationship in a sample

Null Hypothesis

A hypothesis that states there is no relationship in the population

• sample only reflects sampling error

Alternative Hypothesis

A hypothesis that states there is a relationship in the population

• sample reflects population

p-value

The probability of the sample result

Practical Significance

The importance or usefulness of the result in some real-world context

t-test

A test that finds the difference between two means

One-sample t-test

A t-test that compares a sample mean with a hypothetical population mean that provides some interesting standard of comparison

Two-tailed test

A t-test that rejects the null hypothesis if the t-score for the sample is extreme in either direction

One-tailed test

A t-test that rejects the null hypothesis only if the t-score for the same is extreme in one direction specified before data collection

Dependent-samples t-test

A t-test that compares two means for the same sample tested at two different times or under two different conditions

• pretest-posttest, within-subjects

Independent-samples t-test

A t-test that compares the means of two separate samples

• M₁ vs M₂

One-way ANOVA

A test that compares the means of more than two samples

• between-subjects with a single IV

Mean Squares Between Groups (MS_B)

An estimate of population variance based on differences among sample means

Mean Squares Within Groups (MS_W)

An estimate of population variance based on differences among scores within each group

Repeated-Measures ANOVA

A test that measures the dependent variable multiple times for each participant

Factorial ANOVA

A test that includes more than one IV in a factorial design

Type 1 Error

The error of rejecting the null hypothesis when it’s true

• concluding there’s a relationship when there actually isn’t

Type 2 Error

The error of retaining the null hypothesis when it’s actually false

• concluding no relationship when there actually is one

Statistical Power

The probability of rejecting the null hypothesis given the sample size and expected relationship strength

Bayesian Statistics

An approach in which the researcher specifies the probability that the null hypothesis and any important alternative hypotheses are true before conducting the study, conducts the study, and then updates the probabilities based on the data.