Guide to statistical methods

Pick the method that fits your question. Each page explains when to use it, its assumptions, the outputs you get and a concrete worked example.

All methods

• Descriptive Statistics — Summarize and explore your data in seconds.
• Normality Test — Check whether your data follows a normal distribution.
• Homogeneity of Variances — Test the equality of variances across your groups.
• Student's t-Test — Compare means between one or two groups.
• ANOVA — Compare means across three or more groups.
• Chi-Square Test — Analyze associations between categorical variables.
• McNemar Test — Compare proportions on paired data.
• Correlation — Measure the strength and direction of a linear relationship.
• Intraclass Correlation Coefficient (ICC) — Evaluate the reliability and reproducibility of measurements.
• Multiple Linear Regression — Predict a continuous variable from multiple predictors.
• Logistic Regression — Predict a binary outcome and compute odds ratios.
• Principal Component Analysis (PCA) — Reduce dimensionality and visualize your data.
• K-Means Clustering — Identify natural groups in your data.
• Hierarchical Clustering — Visualize the hierarchical structure of your data.
• PLS-DA (Partial Least Squares Discriminant Analysis) — Discriminate groups in omics or high-dimensional data.
• Random Forest — Predict outcomes and identify the most important variables.