lecture13

# lecture13 - Review: Statistical Power Hypothesis testing...

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± Review: Statistical Power ± Hypothesis testing with the t-test ± A new type of t ± http://www.stat.sc.edu/%7eogden/javahtml/ power/power.html z = M ± ± ² M t = M ± s M z = M ± t = M ± s Sample variance Population variance testStatistic = Difference between data and hypothesis standard distance expected by chance

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± M = n = 2 n sample variance = s 2 = SS n ± 1 = SS df M s = s 2 n Estimated standard error t-test population variance not known z-test population variance known 1. State the hypothesis (null and alternative) 2. Set the decision criterion ± Locate the critical t for the speci ed alpha using the appropriate df and table B.2 3. Collect sample & compute statistic ± compute mean, sample variance, and t-statistic 4. Compare statistic to criterion and make a decision ± apply same logic as with a z-test (if obtained t falls in critical region, then reject the null) ± 36 workers are switched to a compressed schedule (3 consecutive 13-hour shifts). They rate their preference on a scale from 1-10 (0=prefer standard, 5=no preference, 10= prefer compressed). 1. State hypothesis ± H 0 : 2 =5 ± H 1 2 ± 5 2. Set Criterion ± ± =0.05, two-tailed ± df=n-1=36-1=35 ± t critical (35)=²2.031
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## lecture13 - Review: Statistical Power Hypothesis testing...

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