Which Statistical Test Should I Use? A Practical Decision Guide
The correct test is not determined by a variable name or a normality p value. It follows from the research question, study design, outcome scale, dependence structure, and assumptions of the model.
Step 1: What are you trying to estimate?
Clarify the primary question before opening any software:
- Compare a mean or distribution between groups.
- Compare a proportion or count.
- Measure association between variables.
- Predict an outcome from several variables.
- Estimate time until an event.
- Evaluate measurement reliability or latent structure.
The same dataset can support different analyses because each question targets a different quantity.
Step 2: What type of outcome do you have?
| Outcome | Common model family |
|---|---|
| Continuous measurement | t test, ANOVA, linear regression |
| Binary outcome | Chi-square methods, logistic regression |
| Count | Poisson or negative-binomial regression |
| Ordered categories | Ordinal methods |
| Nominal categories | Multinomial or contingency-table methods |
| Time to event | Kaplan-Meier and survival regression |
Check whether the measurement scale and distribution support the method. A bounded score with strong ceiling effects may not behave like an unrestricted continuous outcome.
Step 3: Are observations independent, paired, or clustered?
This decision often matters more than the marginal distribution.
- Two independent groups: independent-samples t test or an appropriate alternative.
- The same participants measured twice: paired t test or paired alternative.
- Three or more independent groups: one-way ANOVA or an appropriate alternative.
- Repeated measurements across several times: repeated-measures or mixed modeling.
- Participants within clinics, schools, or households: multilevel or cluster-aware methods.
Analyzing paired or clustered observations as independent usually understates uncertainty.
Step 4: Do you need adjustment?
A simple group comparison answers an unadjusted question. If the goal includes baseline adjustment, confounding control, effect modification, or prediction, use a regression framework suited to the outcome.
Do not add covariates solely because they are available. Their inclusion should follow the design, causal reasoning, protocol, or predictive goal.
Quick method map
| Research question | Starting method |
|---|---|
| Is one mean different from a reference value? | One-sample t test |
| Do two independent means differ? | Independent t test or Welch t test |
| Do paired means differ? | Paired t test |
| Do three or more means differ? | ANOVA |
| Are two categorical variables associated? | Chi-square test or Fisher's exact test |
| Are two continuous variables associated? | Correlation or regression |
| What predicts a binary outcome? | Logistic regression |
| Does event-free time differ? | Kaplan-Meier comparison or Cox regression |
This table is a starting point, not a substitute for checking design details.
Parametric or nonparametric?
Avoid choosing only from a normality test. Consider the estimand, residual behavior, sample size, outliers, scale, variance structure, and robustness. A rank-based test may answer a different question from a test of means.
Welch methods can address unequal variances for many mean comparisons. Transformation, robust methods, generalized models, and bootstrap intervals may also be relevant.
What should you verify before running the test?
- The unit of analysis matches the research question.
- Group coding and reference categories are correct.
- Missing-data handling is documented.
- Independence, pairing, and clustering are represented.
- Assumptions are checked with appropriate diagnostics.
- Effect sizes and confidence intervals are planned.
- Multiple testing is controlled when required.
Use the interactive DataStatPro statistical test selector to move from these decisions to a candidate method. Then consult the analysis index and the method-specific Knowledge Base guide before interpreting results.
Frequently asked questions
Can a normality test choose my statistical test?
No. Normality is only one consideration, and it usually applies to model residuals or sampling behavior rather than every raw variable in isolation.
What is the difference between a t test and ANOVA?
A t test commonly compares two means. ANOVA provides an omnibus comparison for multiple groups or more complex factor structures.
When should I use Fisher's exact test instead of chi-square?
Fisher's exact test is useful when contingency-table counts are sparse and the chi-square approximation is questionable. The choice also depends on table dimensions and the intended inference.
Should I test every variable in my dataset?
No. Analyses should follow prespecified research questions or a clearly labeled exploratory plan. Testing everything increases false-positive risk and weakens interpretability.