The main purpose of a t-test is to test whether two group means are significantly (or meaningfully) different from one another

Between-groups When there are two experimental conditions and different participants were assigned to each condition Otherwise called independent-samples, independent-measures, independent-means Repeated-measures When there are two experimental conditions and the same participants took part in both conditions of the experiment Otherwise called paired-samples, dependent-means, matched-pairs

Independent samples t- tests assumptions include the level of measurement (DV interval or ratio) random sampling, normality and Homogeneity of variance

Repeated-measures t-test assumptions include level of measurement (DV interval or ratio) random sampling and normality

ANOVA = Analysis of Variance

ANOVA’s measure and compare the variance of more than two conditions.

F-statistic represents the ratio of the model to its error. Significant F-statistic shows there is a difference between the groups, but not where the difference is.

F = Variability Between Groups / Variability Within Groups = (Random Error + Treatment Effect)/Random Error. If null is true, treatment effect = 0

Between Conditions = Effect caused by our models conditions

Within Conditions = Error

Family-wise Error Rate (FWER) is the probability of at least 1 false positive when multiple comparisons are being tested. This probability increases for each t-test conducted.

Mean Squares is calculated to eliminate bias associated with the number of scores used to calculate

When an F-ratio is large, it means more variability

A factor is an independent variable

An ANOVA is significant if the f-score is larger than the f-critical