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Independent samples t test

Hypothesis testing. z-score testCompare sample to a known populationPopulation variance is knownOne-sample t testCompare sample to a known population Population variance is unknownIndependent samples t testCompare two samples from unknown populationsBoth population variances are unknownBe

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Independent samples t test

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    1. Independent samples t test

    2. Hypothesis testing z-score test Compare sample to a known population Population variance is known One-sample t test Compare sample to a known population Population variance is unknown Independent samples t test Compare two samples from unknown populations Both population variances are unknown Between-subjects design Related samples t test (paired t test) Compare sample difference between two conditions Population difference and variability are unknown Within-subjects design

    3. Hypothesis testing So far, we have looked only at using one sample as the basis for drawing conclusions about one population However, it is common in research to compare two (or more) sets of data. E.g.: - compare men's and women's attitudes toward abortion - compare the effectiveness of two teaching methods - compare patients' level of depression before and after therapy

    4. Two general research strategies Independent samples the two sets of data come from completely separate samples e.g., men and women an independent-measures t test is used between-subjects design Related samples the two sets of data could come from the same sample e.g., people with depression, before and after therapy a related-samples t test is used within-subject design

    5. Independent samples t test

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