# t test - 15 t tests One Sample Two Independent Samples Two...

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Chapters 13, 14 & 15: t tests, One Sample, Two Independent Samples, Two Related Samples, and Significance Test for Pearson’s r t Test for One Sample X-bar = sample mean μ hyp = population mean s X-bar = standard error of the mean based on the sample standard deviation (s/(sqrt n). Critical value for t is looked up in Table B (p. 520), with df = n-1 Assumptions : Use t rather than z whenever, as almost always is the case, the population standard deviation is unknown. You must also assume that the underlying population is normally distributed . However, even if this normality assumption is violated, t retains its accuracy as long as the sample size is not too small (e.g., as small as 10, CLT). t Test for Two Independent Samples t = (X-bar 1 - X-bar 2 ) – ( μ 1 - μ 2 ) / s X-bar1-X-bar2 X-bar 1 and X-bar 2 = two sample means μ 1 - μ 2 = hypothesized difference of zero between population means (remember that we’re testing to see if the difference between the two groups is bigger than the difference between the two population means (and this difference is 0)) s X-bar1-X-bar2

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## This note was uploaded on 04/30/2008 for the course PSY 320 taught by Professor Harkins during the Spring '08 term at Northeastern.

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t test - 15 t tests One Sample Two Independent Samples Two...

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