Conditional inference trees (ctrees) implement recursive partitioning using permutation‑based hypothesis tests rather than impurity‑based split criteria. At each node, the algorithm tests global independence between the response and each predictor, selects the variable with the smallest p‑value, and then determines the optimal split via a secondary test. This framework removes the variable‑selection bias inherent in […]
Analyzing repeated‑measures data is a common task in psychology, education, basic science, health sciences, and many other fields where the same experimental units are measured across multiple time points or conditions. Traditionally, researchers have relied on repeated‑measures ANOVA to analyze these designs. While repeated‑measures ANOVA is familiar and easy to implement, it comes with several
ANCOVA (analysis of covariance) is a widely used statistical method for comparing the mean values of multiple groups on a continuous outcome while adjusting for the influence of a related continuous variable. ANCOVA extends ANOVA (analysis of variance) by integrating a continuous independent variable into the model to minimize bias and control for potential confounding
In this Statistics Note, we learn how to create Q-Q plots from the scratch in R. A quantile-quantile plot (Q-Q plot for short) is a scatter plot that shows how one empirical distribution approximates a theoretical distribution. Before we dive into QQ plots, let’s clarify an important concept: quantiles. What is a quantile? A quantile
Confounders are undesirable variables that affect the relationship between two variables of interest in a study. For example, in epidemiological research studying the effects of a nutrient on a health issue, such as diabetes, the participant’s body size or physical activity can confound the relationship between the nutrient and the health issue. Therefore, it is