13 - Interpreting Results

13 - Interpreting Results - Chapter 13: Interpreting the...

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Unformatted text preview: Chapter 13: Interpreting the Results of Hypothesis Tests Significance vs. Importance Effect Size Errors in Hypothesis Testing Power of tests Significant only means unlikely to occur by chance if H O is true Doesnt necessarily mean important or useful This assessment depends crucially on the research question of interest Direct relation between effect size and p-value Larger sample sizes will increase significance (decrease p) However we can still use statistics to determine whether the effects of are study are big or small n s X t X hyp / - = Effect Sizes Effect Size: Estimate of the degree to which the effect is present in the population Usually difference between hyp and true There are three standard measures of effect size: Cohens d Hedges g Pearsons r Cohens d Effect (difference between true and hyp ) in standard deviation units Cohens d 2200 true = sample mean; hyp = value when H is true You can use this when you know hyp true d- = Hedges g You can use this when estimating using s x ( 29 1 2--- =- =- n X X X s X g hyp X hyp m m Example Our developmental psychologist from Chapter 12 concluded that the infants in her region have a mean speaking age (14.3 months) that is different from the national average (13 months) How big is this effect?...
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This note was uploaded on 04/17/2008 for the course PSY 201 taught by Professor Arthur during the Spring '08 term at Purdue University-West Lafayette.

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13 - Interpreting Results - Chapter 13: Interpreting the...

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