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Unformatted text preview: More About Hypothesis Testing You should know . . . • Significance • 4 possible outcomes – Type I and Type II error – Alpha; Beta; Power • Effect and distributions – when H is false – when H is true – calculating effect size • More about z tests — assumptions Statistical Significance • When the difference between means is large enough for us to reject the null hypothesis ( H ), we say that the difference is “significant” Decisions vs. Reality • The decision made in a hypothesis test is based on probability — it may be wrong • If we compare reality with the decision that we make, we logically have 4 possible outcomes... 4 Possible Outcomes • 1. Correct rejection of H • the difference is real; you rejected H , and you should have • 2. Incorrect rejection of H Type I error • your test pointed you to a significant difference, but it was just a result of sampling error (that is, chance) 4 Possible Outcomes • 3. Correctly retain H • there isn’t really a difference (perhaps the treatment did nothing), and the test pointed to that assessment • 4. Incorrectly retain H Type II error • there actually is a difference, but sampling error related to the other population (e.g., Facebook users) caused a failure to detect it 4 possible outcomes of a hypothesis test Status of H True H False H Decision: Retain H Correct Decision Type II Error Reject H Type I Error Correct Rejection Decision matches reality 4 possible outcomes of a hypothesis test Status of H True H False H Decision: Retain H Correct Decision Type II Error Reject H Type I Error Correct Rejection “False Alarm” Decision does not match reality 4 possible outcomes of a hypothesis test Status of H True H False H Decision: Retain...
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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.
 Fall '07
 Pfordesher

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