The variable that you care about must be spread out in a normal way. If the variable that you care about is a proportion (48% of males voted vs 56% of females voted) then you should probably use the McNemar Test instead. Some good examples of continuous variables include age, weight, height, test scores, survey scores, yearly salary, etc. Continuous means that the variable can take on any reasonable value. The variable that you care about (and want to see if it is different between the two groups) must be continuous. Let’s dive in to each one of these separately. The assumptions for the Paired Samples T-Test include: Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate. The Paired Samples T-Test is also called the Paired Sample T-Test, Dependent Sample T-Test and the Paired T-Test.Įvery statistical method has assumptions. Multivariate Multiple Linear Regression.Exact Test of Goodness of Fit (multinomial model).Difference Proportion/Categorical Methods Menu Toggle.Single Sample Wilcoxon Signed-Rank Test.Difference Continuous Methods Menu Toggle.Your StatsTest is Ordinal Logistic Regression.Ordered Categorical Dependent Variable Menu Toggle.Your StatsTest is Multinomial Logistic Regression.Your StatsTest is Linear Discriminant Analysis.Categorical Dependent Variable Menu Toggle.Your StatsTest is Multiple Logistic Regression.Your StatsTest is Mixed Effects Logistic Regression.More than One Independent Variable Menu Toggle.Your StatsTest is Simple Logistic Regression.Your StatsTest is Multivariate Multiple Linear Regression.Two or More Dependent Variables Menu Toggle.Your StatsTest is Multiple Linear Regression.Your StatsTest is the Mixed Effects Model.More Than One Independent Variable Menu Toggle.Your StatsTest is Simple Linear Regression.Continuous Dependent Variable Menu Toggle.Your Stats Test is Kendall’s Tau or Spearman’s Rho.Your StatsTest is Point Biserial Correlation.Your StatsTest Is The Log-Linear Analysis.Three (or more) Group Variables Menu Toggle.(more than 10 in every cell) Your StatsTest Is The Chi-Square Test Of Independence.(more than 1000 in total) Your StatsTest Is The G-Test.(more than 10 in every cell) Your StatsTest Is The Two-Proportion Z-Test.(less than 10 in a cell) Your StatsTest Is The Fischer’s Exact Test.Your StatsTest Is The Chi-Square Goodness Of Fit Test.Your StatsTest Is The Exact Test Of Goodness Of Fit (multinomial model).

Your StatsTest Is The G-Test Of Goodness Of Fit.

More Than 10 In Every Cell (and more than 1000 in total) Menu Toggle.

Your StatsTest Is The One-Proportion Z-Test.

Your StatsTest Is The Exact Test Of Goodness Of Fit.

Proportional or Categorical Variable of Interest Menu Toggle.

(2 or more group variables) Your StatsTest Is The Split Plot ANOVA.(one group variable) Your StatsTest Is The One-Way Repeated Measures ANOVA.Your StatsTest Is The Kruskal-Wallis One-Way ANOVA.(2 or more group variables) Your StatsTest Is The Factorial ANOVA.(one group variable with covariate) Your StatsTest Is The One-Way ANCOVA.(one group variable) Your StatsTest Is The One-Way ANOVA.Many Samples Tests (3+ groups) Menu Toggle.Your StatsTest Is The Wilcoxon Signed-Rank Test.Your StatsTest Is The Paired Samples Z-Test.Your StatsTest Is The Paired Samples T-Test.Paired Samples (repeated measurements) Menu Toggle.Your StatsTest Is The Mann-Whitney U Test.Your StatsTest Is The Independent Samples Z-Test.Your StatsTest Is The Independent Samples T-Test.Two Sample Tests (2 groups) Menu Toggle.Your StatsTest Is The Single Sample Wilcoxon Signed-Rank Test.Skewed Variable of Interest Menu Toggle.Your StatsTest Is The Single Sample Z-Test.Normal Variable of Interest and Population Variance Known Menu Toggle.Your StatsTest Is The Single Sample T-Test.Normal Variable of Interest Menu Toggle.One Sample Tests (single group) Menu Toggle.Continuous Variable of Interest Menu Toggle.

T test stata