ANOVA or t-test? Chi-square or logistic regression? Stop guessing and learn the decision framework that ensures your statistical analysis matches your research question and data.
Engr. Rafiq Hasan
GIS & Remote Sensing Specialist
Choosing a statistical test should not feel like throwing darts at a board. Yet for many researchers, it does. The result is often a mismatch between the research question, the data, and the analytical method — a mismatch that can invalidate conclusions and invite criticism from examiners and reviewers.
The first question is not which test to use but what kind of question you are asking. Are you comparing groups? Testing relationships? Predicting outcomes? Examining change over time? Your research question determines the family of statistical methods that are appropriate, and everything else flows from there.
Start with your variables. Identify your independent variable (the predictor) and your dependent variable (the outcome). Then determine the type of each variable: categorical (nominal or ordinal) or continuous (interval or ratio). This single piece of information eliminates most of the statistical menu.
If you are comparing two groups on a continuous outcome — for example, test scores of male versus female students — a t-test is appropriate if the outcome is normally distributed. If the normality assumption is violated, the Mann-Whitney U test is the non-parametric alternative. For comparing three or more groups, use ANOVA (or the Kruskal-Wallis test for non-normal data). Using multiple t-tests instead of ANOVA inflates your Type I error rate — a common but avoidable mistake.
If you are examining the relationship between two continuous variables — for example, income and life satisfaction — Pearson correlation is the starting point. For non-normal data or ordinal variables, use Spearman's rho. Remember that correlation measures association, not causation, and a strong correlation does not mean the relationship is linear.
If you are predicting a continuous outcome from one or more predictors, linear regression is the workhorse. For binary outcomes (yes/no, pass/fail), logistic regression is appropriate. For categorical outcomes with more than two levels, multinomial logistic regression. For count data, Poisson or negative binomial regression. For time-to-event data, survival analysis.
If you are examining change over time within the same subjects — for example, pre-test and post-test scores — a paired t-test is appropriate for two time points. For three or more time points, repeated measures ANOVA or a linear mixed model. The mixed model is increasingly preferred because it handles missing data more gracefully and does not require the sphericity assumption.
Check your assumptions. Every statistical test has assumptions — normality, homogeneity of variance, independence of observations, linearity. Violating these assumptions does not necessarily mean your results are wrong, but it does mean you need to justify your choice or use a robust alternative. Report which assumptions you checked and how, so your reader can evaluate the credibility of your analysis.
Report effect sizes, not just p-values. A p-value tells you whether an effect exists; an effect size tells you how large it is. In many fields, a statistically significant result with a tiny effect size is less meaningful than a non-significant result with a moderate effect size. Cohen's d, eta-squared, and odds ratios are commonly reported effect sizes that help readers interpret the practical significance of your findings.
When in doubt, consult a statistician. Statistical consulting is not a sign of weakness — it is a sign of rigor. Most universities offer free statistical support through their research methods centers. A thirty-minute consultation can save you from a fundamental analytical error that would require rewriting an entire chapter.