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 What inferential statistics does best is allow decisions to be made about populations based on the information about samples.  One of the most useful tools for doing this is a test of statistical significance 2

 Inferential statistics test the likelihood that the alternative (research) hypothesis (H 1 ) is true and the null hypothesis (H 0 ) is not. 3

 In testing differences, the H 1 would predict that differences would be found, while the H 0 would predict no differences.  By setting the significance level (generally at.05), the researcher has a criterion for making the following decision: 4

 If the.05 level is achieved (p is equal to or less than.05), then a researcher rejects the H 0 and accepts the H 1.  If the.05 significance level is not achieved, then the H 0 is retained. 5

Alpha levels are often written as the “p-value”. e.g., p =.05; p <.05; (p less than.05) p <.05 (p equal to or less than) (the chance of making 5 in 100 or 1 in 20 of making an error) 7

 Df are the way in which the scientific tradition accounts for variation due to error.  It specifies how many values vary within a statistical test. 8

 It specifies how many values vary within a statistical test Scientists recognizes that collecting data can never be error-free Each piece of data collected can vary, or carry error that we cannot account for By including df in statistical computations, scientists help to account for this error 9

 If reject H0 and conclude groups are really different, it doesn’t mean they’re different for the reason you hypothesized May be other reason 10

 Since H0 is based on sample means, not population means, there is a possibility of making an error or wrong decision in rejecting or failing to reject H0 Type I error Type II error 11

 Type I error – rejecting H0 when it was true (it sound have been accepted)  If alpha =.05, then there’s a 5% chance of Type 1 error. 12

 Type II error – accepting H0 when it should have been rejected  If increase alpha, you will decrease the chance of Type II error 13

 One variable  One-way chi-square  Two variables ( 1 IV with 2 levels; 1 DV)  t-test  Two variables ( 1 IV with 2+ levels; 1 DV)  ANOVA 14

 Three or more variables  ANOVA  See handouts for more other examples of inferential statistics 15

 Students will state what they have learned in Lecture