Hypothesis Testing.

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Introduction to Hypothesis Testing

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Presentation transcript:

Hypothesis Testing

Hypothesis Testing We talked about this the first day. Let’s talk more about it now. Four steps: Formulate Null hypothesis and Alternative hypothesis (hypothesis) Obtain sampling distribution and rejection regions (population) Calculate test statistic from your data (sample) Compare the test statistic to rejection regions (if within rejection region, then significant)

Hypothesis Formulate Null and Alternative hypothesis Null hypothesis H0 : Population means equal If two groups: m1 = m2 If three+ groups: m1 = m2 = m3 = m4 Alternative hypothesis: H1 : mean NOT equal If two groups:: m1 ≠ m2 If three+ groups: m1 ≠ m2 ≠ m3 ≠ m4 As a researcher, you want to reject the “null”.

Errors in Hypothesis Testing REALITY H0 is true (n.s.) H0 is false (sig) Probability = a Probability = (1 - b) Reject H0 (sig) (test statistic is in Rejection region) Type I error, or “False Alarm” Correct Decision POWER OUTPUT FROM SPSS Fail to Reject H0 (n.s.) Probability = b I GOT THIS SLIDE FROM SOMEWHERE, NOT MINE Probability = (1 - a) (test statistic is not in Rejection region) Type II error, or a “miss” Correct Decision

Factors Increasing Power: (1) Increasing alpha level (e.g., .05 to .09) (2) Increasing difference between means (3) Reducing between-group variation (4) Increasing sample size