# Statistics

 Each level of each independent variable has different subjects. Between-Subjects or Independent Group Design Each subject participates in all levels of all independent variables. Within-Subjects or Repeated Measures Group Design There must be at least two independent variables. Each subject participates in all levels of one independent variable but not the other. Mixed Group Design when the subjects try to figure out the experiment and then alter their behavior to either "help" the scientist or even hinder the scientist Demand Characteristics Standard Error of the Mean Standard Error of a Sample when you reject the null hypothesis when shouldn't have because the null hypothesis is actually true - there is not difference between your groups. Type I error when you fail to reject the null hypothesis when you should have because there really is a significant difference between your groups. Type II Error If the scientific hypothesis predicts a direction of the results, we say it is a One-Tailed Hypothesis If the scientific hypothesis does not predict a direction of the results, we say it is a Two-Tailed Hypothesis an analysis of an experimental design with one independent variable and a nominal dependent variable One-Way Chi-Square Chi-Square df = k -1 Degrees of freedom for a Chi-Square fe of a Two-Way Chi-Square when you have two independent variables and a nominal dependent variable Two-Way Chi-Square df = (number of rows -1) x (number of columns -1) Degrees of Freedom for a Two-Way Chi-Square If your sample size is above 1000 (Comparing Sample to a Population Single Sample z-test Single Sample z-test formula If your sample size is below 1000 (Comparing Sample to Population) Single Sample t-test Single Sample t-test formula If your two sample groups are independent of each other t-test for Independent Groups t-test for Independent Groups formula Standard Error of the Difference for Independent Groups (n1 - 1) + (n2 - 1) df independent groups If the two samples are not independent of each other but instead are positively correlated to each other t-test for Correlated Groups the standard error of the difference(correllated groups) number of pairs - 1 df correlated groups t-test for correlated samples: using raw data D bar The mean of all the difference scores. Difference scores are calculated by subtracting each Y value from its X pair value Standard Difference for Correlated Groups using the raw data F = MSbg / MSwg F ratio formula One-Way ANOVA MSbg = SSbg / dfbg MSbg formula One-Way ANOVA MSwg = SSwg / dfwg MSwg formula One-Way ANOVA dfbg = k - 1 dfbg formula One-Way ANOVA dfwg = (n1 - 1) + (n2 - 1) + . . . + (nk - 1) dfwg formula One-Way ANOVA SSbg = [ (ΣX1)2 / n1 ) + (ΣX2)2 / n2 ) + . . . + (ΣXk)2 / nk ) ] - [ (ΣX1 + ΣX2 + . . . + ΣXk )2 / Ntotal ] SSbg formula One-Way ANOVA SSwg = [ (ΣX21 + ΣX22 + . . . + ΣX2k ) ] - [ (ΣX1)2 / n1 ) + (ΣX2)2 / n2 ) + . . . + (ΣXk)2 / nk ) ] SSwg formula One-Way ANOVA dfN dfbg dfD dfwg Nominal Dependent Variable Data Chi-Square (X2) Ordinal Dependent Variable Data An ordinal statistic Interval/Ratio Dependent Variable 2+ Factors (Independent Variables) Two-Way ANOVA Interval/Ratio Dependent Variable 1 Factor (Independent Variables) 2 Levels (i.e. control and experiment) T-Test Interval/Ratio Dependent Variable 1 Factor (Independent Variables) 3+ Levels (i.e. control, experiment1, experiment2) One-Way ANOVA AuthorMental86 ID2812 Card SetStatistics Descriptionstatistics final Updated2009-12-11T10:08:56Z Show Answers