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9 terms

Psych 12 Final (One Factor Designs)

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Inferential statistics
How do we know whether group differences are big enough? Used to test hypothesis about data and to draw conclusions about reliability and generalizability of findings (t-tests, ANOVAs, chi-square)
Scores differ for two reasons
-Variation between groups (good)
-Variation within groups (bad)
-We want big variation between groups relative to variation within groups
How inferential statistics work
-estimate variation within groups and between groups
-estimate how much the groups differ by chance alone (use critical values)
-determine whether your group differences are larger than the difference expected by chance alone
P-values
-an estimate of the probability that your effect (group differences) is due to chance alone
-arbitrary cut-off 0.5 (5% probability is due to chance, that is a fluke)
T-tests for 2-group designs: statistical difference: sample size
-typically for experiments & quasi-experiments
-compares the mean difference between 2 groups to variation within each group
Effect size measures
-(cohen's d, eta-squared)
-important in eliminating the influence of sample size
Designs with 3 or more groups (1 IV)
-Type 1 error
-alpha level (5% probability of type 1 error)
-the chance the test will show an effect when none exists
-Type 2 error
-beta level (20% probability of type 2 error)
-the chance the test will not show an effect when one does exist
Alpha inflation
as we conduct more and more statistical tests, the chance of a Type 1 error increases across the entire collection of tests
One-way ANOVA
-omnibus tests - checks to see if there is any difference between groups
-F statistic - life the t-test
-if one-way ANOVA is significant