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Lecture #7.docx

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PSYC 2021
Agnieszka Kopinska

Lecture #7  Hypothesis Test: in stats, we ask questions. We want to answer them with certainty. Basic Logic of Hypothesis Testing  One sample mean test it is rarely done. You usually compare groups with it. o For example: after 9/11 or earthquake in Japan, find population scores of happiness/stress levels. You can’t study what happens before and after, so use theoretical values.  Theoretical values for μ (population mean) and s (sample standard deviation). o Draw sample and deliver treatment. See what happens after. Then take mean of sample for population.  Q: Did my treatment work? A: It didn’t work mean of sample is close to mean of population OR It did work mean of sample is far from mean of population. o Theoretical view: treat it as if you’re treating entire population. o Example: researching depression in rats  μ=28.7 σ=7.6 M=50 After treatment: M=25.1  There are 2 possibilities: 1) The mean after treatment is smaller because the therapy works. 2) By sampling error, I selected happy rats. o Sampling Distribution  Outcome is unlikely under scenario that therapy didn’t work (region near tail end of distribution)  Decision line usually appears at the last 5% in the tail end of the distribution. Anything less that appears after the decision line is part of a rejected region. o Quest
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