Module 2: One-Way ANOVA

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

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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

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**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.

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

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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

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