You should use Point-Biserial Correlation in the following scenario: You can check for this assumption by plotting your continuous variable in each of your two groups and visually identifying if the spread of the data is similar. One of the assumptions of Point-Biserial correlation is that there is similar spread between the two groups of the binary variable. You can tell if your variables have outliers by plotting them and observing if any points are far from all other points. Point-Biserial correlation is sensitive to outliers, or data points that have unusually large or small values. The variables that you care about must not contain outliers. Only use Point-Biserial Correlation on your data if the variable you care about is normally distributed. In statistics, this is called being normally distributed (aka it must look like a bell curve when you graph the data). The variable that you care about must be spread out in a normal way. Some good examples of binary variables include smoker(yes/no), sex(male/female) or any True/False or 0/1 variable. Some good examples of continuous variables include age, weight, height, test scores, survey scores, yearly salary, etc.īinary means that your variable is a category with only two possible values. Continuous means that the variable can take on any reasonable value. Continuous and Binaryįor this test, you should have one continuous and one binary variable. Let’s dive in to each one of these separately. The assumptions for Point-Biserial correlation include: Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate. Point-Biserial correlation is also called the point-biserial correlation coefficient.Īssumptions for Point-Biserial correlationĮvery 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.
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