How to select an appropriate statistical test method based on the research question?

Appropriate statistical test selection involves matching analysis methods to research questions and data characteristics to ensure valid inferences. Feasibility requires assessing data attributes and study design alignment. Key considerations include variable types (e.g., nominal, ordinal, interval/ratio), data distribution normality, independence of observations, group sizes, and specific hypotheses. Parametric tests (e.g., t-tests, ANOVA, Pearson correlation) assume normality and interval/ratio data; non-parametric alternatives (e.g., Mann-Whitney U, Kruskal-Wallis, Spearman correlation) apply otherwise. Tests comparing group differences require identifying whether groups are independent or paired/matched; examining relationships involves choosing between correlation or regression analysis. Always verify test assumptions before application. First, precisely define the research question and hypotheses. Next, determine the measurement levels of dependent and independent variables and assess data distribution (e.g., using normality tests). Then, identify the analysis goal: comparing group means/proportions (e.g., t-test, chi-square), assessing relationships (e.g., correlation, regression), or predicting outcomes (e.g., linear or logistic regression). Finally, consult statistical guidelines (e.g., flowcharts) or software documentation to confirm the chosen test fits the data structure. Proper selection underpins credible findings.