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### 19 Cards in this Set

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 background for hypothesis testing 1.expose participants to 2 or more levels of IV 2.determine size of f-ratio by comparing between and within group variance 3.set the cut-off for acceptable prob. that results are due to chance (alpha level) 4.calculate actual prob(p-value) P-value less than Alpha level prob that difference is due to chance is acceptable Null hypothesis rejected statistically significant difference b/w the groups P-value more than alpha level prob. that difference is due to chance is not acceptable Null hypothesis is accepted NOT statistically significatn Null hypothesis no difference between experimental and control groups in terms of DV hypothesis of "no difference" observed difference due to change, not IV testing Null hypothesis reject null when prob is small <.05 accept null when prob is large <.05 alternative hypothesis 2 groups are different in terms of DV hypothesis of "differences exist" observed difference due to IV (hopefully) alt.rejected if null accepted alt.accepted if null rejected type I error when reject Null, but shouldnt have results say there is difference between groups..but difference is due to chance prob of type I error=Alpha setting smaller alpha ..and get significant results: conservative more confidence that difference not due to chance if set larger alpha value ..and get significant results: liberal less confidence difference is not due to chance Power =ability of a statistical test to detect a relationship between variables (corr) or differences between groups (exp) when they exist power + alpha level smaller alpha reduces power, bigger alpha increases power requires more between-group variance to show a difference usually experimenter will not set higher than 0.5 power + effect size (overlap between populations) larger(less overlap)=increases power manipulate IV using extreme differences b/w levels to show a difference potential solution: pilot study power + within group variance less within group variance increases power reduce it by reducing reliabilty of DV, consistent treatment of participants power + sample size increasing sample size increases power larger the sample,the more confident we can be that the result was not due to chance determining power can determine # of participants required to get statistically significant findings use large samples ANOVA can test any number of groups simultaneously to look for a difference Tests the Null that no difference among all possible pairings of groups A thru D simultaneously T-test test for differences between 1 pair of groups t a time (ex. AandB, AandC, AandD..) probability pyramiding with 4 levels (6 possible pairings), prob of Type I error increases from 0.5 to 0.26 (26% differences due to chance) with 3 levels, probability of Type I error increases from 0.5 to 0.14 Post-Hoc "after the fact" test tells which pairs of groups are statistically different