8b_More_Hypothesis_Testing_Outline_Slides

# 8b_More_Hypothesis_Testing_Outline_Slides - You should know...

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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 0 is false – when H 0 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 0 ), 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...

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4 Possible Outcomes • 1. Correct rejection of H 0 the difference is real; you rejected H 0 , and you should have • 2. Incorrect rejection of H 0 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 0 there isn’t really a difference (perhaps the treatment did nothing), and the test pointed to that assessment • 4. Incorrectly retain H 0 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 0 True H 0 False H 0 Decision: Retain H 0 Correct Decision Type II Error Reject H 0 Type I Error Decision matches reality 4 possible outcomes of a hypothesis test Status of H 0 True H 0 False H 0 Decision: Retain H 0 Correct Decision Type II Error Reject H 0 Correct Rejection “False Alarm” Decision does not match reality
4 possible outcomes of a hypothesis test Status of H 0 True H 0 False H 0 Decision: Retain H 0 Type II Error Reject H 0 Type I

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