10_t_Test_Independent

10_t_Test_Independent - t Test for Two Independent Samples...

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1 t Test for Two Independent Samples t Test for Two Independent Samples • Null and Alternative Hypotheses t ratio – Pooled variance – Estimated standard error – Degrees of freedom • Assumptions • Cohen’s d • Proportion of Variance Accounted for t Test for Two Independent Samples • Previous t test for one sample compared a single sample mean to a hypothesized population mean • What if we want to compare the means of two samples to determine if the samples are significantly different (not different just by chance)? Comparing Samples Independent-measures design: • Common research design
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2 Comparing Samples Independent-measures design: • Common research design • Different randomly-selected samples experience different versions of a treatment or manipulation – e.g., one group receives Treatment A, the other B General Population Before Treatment General Population Random Sample Before Treatment General Population Random Sample Random Sample Before Treatment
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3 General Population Get Treatment A Get Treatment B Before Treatment Comparing Samples Independent-measures design: • Common research design • Different randomly-selected samples experience different versions of a treatment or manipulation – e.g., one group receives Treatment A, the other B Comparing Samples Independent-measures design: • Common research design • Different randomly-selected samples experience different versions of a treatment or manipulation – e.g., one group receives Treatment A, the other B • A dependent variable that is relevant to the manipulation is then measured Comparing Samples Independent-measures design: • Common research design • Different randomly-selected samples experience different versions of a treatment or manipulation – e.g., one group receives Treatment A, the other B • A dependent variable that is relevant to the manipulation is then measured • The scores are then compared
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4 Comparing Samples Independent-measures design: • Common research design • Different randomly-selected samples experience different versions of a treatment or manipulation – e.g., one group receives Treatment A, the other B • A dependent variable that is relevant to the manipulation is then measured • The scores are then compared • Also called a between-subjects design Comparing Samples Independent-measures design: • because the samples consist of different people, they are considered to be independent of one another • likewise, the groups of scores obtained from the samples are considered independent of one another Comparing Samples Independent-measures design: • The groups are considered samples that represent populations • Through the samples, the populations are compared after treatment Comparing Samples H 1 ( ) claims that the populations are different – i.e., the treatments have different effects on scores
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5 Comparing Samples H 1 ( ) claims that the populations are different – i.e., the treatments have different effects on scores
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This note was uploaded on 08/22/2011 for the course PSY 207 taught by Professor Pfordesher during the Fall '07 term at SUNY Buffalo.

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10_t_Test_Independent - t Test for Two Independent Samples...

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