LESSON 50 - CHOOSING THE RIGHT STATISTICAL TESTS: FACTORS TO CONSIDER WHEN CHOOSING A TEST

Choosing the right statistical test hinges on matching the analysis to the research question and the data’s measurement properties—because the test selected determines what conclusions can legitimately be drawn. The lesson frames statistical tests as the decision point for rejecting or failing to reject a null hypothesis, while “statistical tools” are the specific procedures used to analyze and present data. For example, correlation is the test category, but Pearson or Spearman are the tools chosen within that category depending on whether the variables are continuous or ordinal/ranked.

The first factor is the research question itself—especially whether the goal is to examine relationships (e.g., how X influences Y) or to compare group means (e.g., differences between groups). This choice aligns with three broad forms of statistical testing: correlation, regression, and comparing means. Next comes the scale of measurement: variables measured at nominal or ordinal levels call for tools suited to categorical data, while interval or ratio measurements call for tools suited to continuous data. The lesson emphasizes that scale is not a technical afterthought; it directly constrains which statistical tools are appropriate.

Dependent variables drive many of the remaining decisions. The dependent variable’s type—categorical (binary, ordinal, nominal) versus continuous (interval/ratio)—determines which family of tests can be used. The lesson also distinguishes variable roles: if the dependent variable is categorical, tools like Spearman (for ordinal/ranked relationships) and logistic regression (for prediction with categorical outcomes) become relevant, whereas continuous dependent variables align with Pearson correlation and linear regression approaches.

The number and structure of variables matter as well. Regression decisions depend on how many independent variables are included: simple linear regression fits one independent variable, while multiple linear regression fits more than one. For comparing means, the number of groups and whether measurements are repeated within the same subjects shape the test choice. Independent t-tests apply when comparing two independent groups; one-way ANOVA (described as “one way another”) applies when there are more than three groups. When the same participants are measured repeatedly, paired t-tests handle two related measurements, and repeated measures ANOVA handles more than three.

Other practical considerations include whether measurements are repeated for each subject and the type of data available (continuous vs categorical; binary vs quantitative vs ranked). The lesson also links clarity of research questions to conceptual frameworks: drawing a conceptual framework with indicators helps ensure variables are measured in a way that supports correct statistical selection.

Finally, the lesson reiterates the parametric versus non-parametric distinction. Parametric tests require assumptions (covered in a previous lesson), while non-parametric tests are treated as “assumption-free” in the sense that they make very few assumptions—such as not requiring data to come from a normally distributed population. By the end, the takeaway is a decision checklist: use the research question, scale of measurement, dependent-variable type, number of variables, comparison versus relationship goal, and repeated-measures structure to select the correct statistical test category and then the matching statistical tool.