Statistical Concepts
Plain-language tutorials covering everything from effect size to regression. Each guide includes formulas, worked examples, software implementation in R, Python, SPSS, and Excel, and APA reporting templates.
Descriptive Statistics
Inferential Statistics
Chi-Square Test of Independence
Learn how to perform a chi-square test of independence to analyze associations between categorical variables, with formulas, examples, and Cramer's V.
beginnerIndependent Samples t-Test
Learn how to conduct and interpret an independent samples t-test, including assumptions, formulas, worked examples, and APA reporting guidelines.
beginnerKruskal-Wallis H Test
Learn how to conduct and interpret a Kruskal-Wallis H test, the non-parametric alternative to one-way ANOVA, with formulas, a worked example, and APA reporting guidelines.
intermediateLogistic Regression
Learn how to conduct and interpret binary logistic regression: predict a dichotomous outcome from one or more predictors, calculate odds ratios, and assess model fit.
advancedMann-Whitney U Test
Learn how to conduct and interpret a Mann-Whitney U test, the non-parametric alternative to the independent t-test, with formulas, a worked example, and APA reporting guidelines.
intermediateMultiple Linear Regression
Learn how to conduct and interpret multiple linear regression: predict a continuous outcome from two or more predictor variables, assess model fit with R-squared, and check for multicollinearity.
advancedOne-Way ANOVA
Learn how to conduct a one-way ANOVA to compare three or more group means, including F-ratio formulas, post-hoc tests, and effect size with eta-squared.
intermediatePaired Samples t-Test
Learn how to conduct a paired samples t-test for pre/post designs and repeated measures, with formulas, worked examples, and APA reporting format.
beginnerPearson Correlation
Learn how to calculate and interpret the Pearson correlation coefficient (r) to measure the strength and direction of linear relationships between two variables.
beginnerRepeated Measures ANOVA
Learn how to conduct and interpret a repeated measures ANOVA: compare means across three or more time points or conditions from the same participants, test sphericity, and apply corrections.
advancedSimple Linear Regression
Master simple linear regression: learn how to predict a continuous outcome from one predictor variable, interpret slope, intercept, and R-squared values.
intermediateTwo-Way (Factorial) ANOVA
Learn how to conduct and interpret a two-way ANOVA, including main effects, interaction effects, formulas, a worked example, and APA reporting guidelines.
intermediateWilcoxon Signed-Rank Test
Learn how to conduct and interpret a Wilcoxon signed-rank test, the non-parametric alternative to the paired t-test, with formulas, a worked example, and APA reporting guidelines.
intermediateEffect Size & Power
Effect Size
Learn what effect size is, why it matters more than p-values alone, and how to calculate and interpret Cohen's d, Hedges' g, and eta-squared for your research.
beginnerSample Size Determination
Learn how to calculate the right sample size for your research study using power analysis, effect size estimates, and practical planning considerations.
intermediateStatistical Power & Power Analysis
Learn what statistical power is, why 80% is the standard threshold, and how to conduct a power analysis to determine if your study can detect real effects.
intermediateReliability & Validity
Cronbach's Alpha
Understand Cronbach's alpha for measuring internal consistency reliability. Learn the formula, interpretation guidelines, and what to do when alpha is low.
beginnerInter-Rater Reliability
Learn how to assess inter-rater reliability using Cohen's Kappa and ICC. Understand percent agreement, chance correction, and interpretation benchmarks.
intermediate