Which Statistical Test Should I Use? A Practical Decision Guide for Health and Medical Research

“Which statistical test should I use?” is usually not a question that can be answered from the variable names alone. Statistical test selection depends on what you want to estimate or compare, the type of outcome, how observations were obtained, whether observations are independent or paired, how many groups or conditions are involved, and what assumptions are reasonable for the data.

A useful way to choose a statistical test is therefore to work from the research question and data structure toward the method, rather than starting with a list of tests in a software menu. Altman explicitly organizes methods around problems such as comparing groups with continuous or categorical data, relationships between variables, paired versus independent observations, and survival times rather than around the names of statistical procedures (Altman, 1991).

This resource provides a practical framework for choosing a statistical test in health and medical research. It focuses on common unadjusted comparisons and indicates when the problem has moved beyond a simple test into regression, survival analysis, or another modeling framework.

The Short Answer: Choose the Test in This Order

Before choosing a statistical test, answer these questions:

1. What is the research goal? Are you estimating a quantity, comparing groups, testing association, or studying time until an event?
2. What is the outcome type? Continuous, binary, nominal categorical, ordinal, count/rate, or time-to-event?
3. What is the data structure? Independent groups, matched pairs, repeated observations, or another dependent structure?
4. How many groups or conditions are being compared? One, two, or more than two?
5. What parameter or feature is scientifically relevant? For example, a mean difference, proportion difference, odds ratio, distributional difference, or survival contrast.
6. Are the assumptions of the candidate method reasonable?
7. What estimate and uncertainty should accompany the test?

Rosner’s statistical-inference flowchart follows essentially this logic: it distinguishes one- and two-sample problems, independent versus paired samples, normal-theory versus nonparametric approaches, continuous versus binary outcomes, and survival-analysis problems (Rosner, 2016).

Statistical Test Selection: A Practical Decision Table

The table below is a starting framework rather than an automatic rule. Candidate methods still need to be checked against their assumptions and the exact scientific question.

Starting framework for selecting candidate statistical methods

Research goal	Outcome type	Design/data structure	Candidate method	Major considerations	Common mistake
Compare a continuous outcome with a reference value	Continuous	One sample	One-sample t test	Normal-theory assumptions or adequate large-sample justification	Testing a mean when another feature of the distribution is the real target
Compare a continuous outcome between two groups	Continuous	Two independent groups	Two-sample t test; unequal-variance version when appropriate	Independence, distribution of the outcome, variance assumptions for the chosen version	Treating independent groups as paired, or automatically assuming equal variances
Compare a continuous outcome between two conditions	Continuous	Paired observations	Paired t test	Analysis is based on within-pair differences; distributional assumptions concern those differences	Applying an independent-samples test to paired data
Compare two independent groups without relying on normal-theory assumptions	Ordinal or quantitative/rankable	Two independent groups	Mann–Whitney/Wilcoxon rank-sum test	Independent samples; interpretation depends on the distributional question and assumptions	Calling it automatically a “test of medians”
Compare paired observations nonparametrically	Ordinal or quantitative/rankable	Paired observations	Wilcoxon signed-rank test; sign procedure for a different, weaker-assumption location question	Pairing must be retained; signed-rank procedures have assumptions beyond merely “non-normal data”	Treating nonparametric as meaning assumption-free
Compare a continuous outcome across more than two independent groups	Continuous	Independent groups	One-way ANOVA	Independence and the normal-theory/variance assumptions appropriate to the analysis	Running many separate two-sample tests instead of addressing the overall comparison
Compare more than two independent groups nonparametrically	Ordinal or quantitative/rankable	Independent groups	Kruskal–Wallis test	Independent samples; rank-based distributional comparison	Assuming it answers exactly the same estimand as ANOVA
Compare more than two related conditions nonparametrically	Ordinal or quantitative/rankable	Matched/repeated/block structure	Friedman-type procedure	Dependence/block structure must be represented	Using Kruskal–Wallis as though repeated measurements were independent
Compare proportions or examine association	Binary/categorical	Independent observations	Contingency-table methods, including chi-square procedures; exact methods where appropriate	Sampling structure, table dimensions, and adequacy of large-sample approximations	Using a continuous-outcome test for categorical responses
Compare paired binary responses	Binary	Matched/paired observations	McNemar-type analysis	Analysis must reflect matched pairs	Using an ordinary independent 2 × 2 chi-square test
Model a binary outcome using predictors	Binary	Regression setting	Logistic regression	Correct outcome structure, model specification, dependence structure	Treating a multivariable question as a sequence of separate two-group tests
Model an unordered multicategory outcome	Nominal	Regression setting	Baseline-category/multinomial logit model	Categories have no inherent ordering	Imposing an ordinal model on nominal categories
Model an ordered categorical outcome	Ordinal	Regression setting	Ordinal-response model such as a cumulative-logit model	Ordering is meaningful; model-specific assumptions must be assessed	Discarding the ordering or treating categories automatically as continuous
Analyze event counts or rates	Count/rate	Depends on exposure and design	Count/rate methods; Poisson-based methods may be candidates	Mean/variance and exposure structure must match the chosen model	Treating counts as ordinary normally distributed measurements without checking the structure
Compare time until an event	Time-to-event	May include censoring	Survival-analysis methods; log-rank methods for appropriate group comparisons	Event time and censoring are fundamental parts of the data	Analyzing observed survival times as ordinary continuous outcomes

The distinction between ordinary continuous-outcome analysis and survival analysis is especially important. Survival data can include individuals for whom the event has not been observed by the end of follow-up; Altman treats these as censored observations and uses dedicated survival methods to estimate survival probabilities and compare survival experience (Altman, 1991).

Step 1: Start With the Research Question, Not the Variable List

Two datasets containing exactly the same variables can require different analyses because the investigators are asking different questions.

A researcher might want to:

• estimate a population quantity;
• compare an outcome between groups;
• determine whether two categorical variables are associated;
• examine a relationship between continuous variables;
• adjust a comparison for other variables;
• model a binary or ordinal response; or
• compare time-to-event experience.

These are different statistical problems.

Altman separates methods according to problems such as comparing groups, studying relationships among variables, paired observations, and survival times. Rosner similarly routes inferential decisions according to the type of problem and outcome rather than offering a single universal test-selection rule (Altman, 1991; Rosner, 2016).

So, before asking “Which test?”, write the statistical question in words. For example: Is the goal to compare two independent groups with respect to a continuous outcome? That description already eliminates many irrelevant methods.

Step 2: Identify the Outcome Type

The outcome usually provides the next major branch in the decision tree.

Continuous outcomes

For quantitative outcomes where means are scientifically meaningful, common normal-theory methods include t tests for one- or two-group problems and ANOVA for comparisons involving multiple groups. The exact method also depends on pairing and assumptions (Altman, 1991; Rosner, 2016).

Binary outcomes

A binary outcome has two possible categories. Analyses commonly focus on proportions or associations in contingency tables, while regression problems with a binary response lead naturally to logistic-regression methods. Rosner’s inference framework explicitly separates binomial outcomes from continuous outcomes, while Agresti develops categorical-response inference and logistic models for such data (Rosner, 2016; Agresti, 2013).

Nominal categorical outcomes

Nominal outcomes contain multiple categories without an inherent ordering. Contingency-table methods may be suitable for simple comparisons or association questions. For regression with a nominal response, Agresti describes baseline-category logit models as a natural modeling framework (Agresti, 2013).

Ordinal outcomes

Ordinal categories have a meaningful order. That ordering can be used rather than ignored. Agresti distinguishes nominal from ordinal responses and describes cumulative-logit models for ordinal outcomes, while rank-based methods provide another family of procedures for suitable ordinal or rankable outcomes (Agresti, 2013; Hollander et al., 2014).

Count or rate outcomes

Counts and incidence rates are discrete outcomes and may require methods designed for their probability structure rather than ordinary continuous-data procedures. Rosner separately treats incidence-rate inference, reinforcing that rate data constitute a different inferential problem from an ordinary comparison of continuous measurements (Rosner, 2016).

Time-to-event outcomes

Time-to-event data require special attention because some event times can be censored. A participant may remain event-free when observation ends or may leave follow-up before an event is observed. Ordinary analysis of only the observed event times discards this structure; survival-analysis methods are designed to use time and censoring information appropriately (Altman, 1991).

Step 3: Decide Whether Observations Are Independent or Paired

The paired vs independent data distinction is one of the most consequential decisions in statistical test selection.

For continuous paired data, the paired t test works with the within-pair differences rather than treating the two sets of observations as unrelated samples. For rank-based paired analysis, the Wilcoxon signed-rank procedure is a standard candidate. For paired binary responses, McNemar’s test is designed specifically for matched binary data (Rosner, 2016; Agresti, 2013).

Step 4: Count the Groups or Conditions

Once outcome type and dependence structure are clear, ask whether the problem involves one, two, or more than two groups or conditions.

One sample

One-sample methods compare a sample parameter or distributional feature with a specified reference value. Candidates include one-sample t procedures for means under normal-theory assumptions and nonparametric location procedures for other settings.

Two independent groups

For a continuous outcome, a two-sample t test may be appropriate. If the relevant assumptions are unsuitable and the research question fits a rank-based comparison, the Mann–Whitney/Wilcoxon rank-sum procedure may be considered (Altman, 1991; Hollander et al., 2014).

Two paired conditions

The paired t test is a standard mean-based method; Wilcoxon signed-rank methods provide a nonparametric location approach. For paired binary outcomes, McNemar-type methods apply instead.

More than two independent groups

For a continuous outcome, one-way ANOVA provides an overall comparison under its model assumptions. Kruskal–Wallis procedures provide a rank-based approach for a one-way independent-samples layout. Hollander et al. treat Kruskal–Wallis within the nonparametric one-way layout rather than simply as a generic substitute for every ANOVA problem (Hollander et al., 2014).

Step 5: Parametric vs Nonparametric Test Is Not Just a Normality Checkbox

The phrase parametric vs nonparametric test often encourages an oversimplified rule:

Normal data → parametric test; non-normal data → nonparametric test.

That is not a sufficient decision framework.

Hollander et al. describe nonparametric methods as requiring relatively mild assumptions and note that many avoid the traditional normal-population assumption. They also emphasize distribution-free properties of particular procedures and the usefulness of rank-based methods in settings where normal-theory methods are inappropriate (Hollander et al., 2014).

But “nonparametric” does not mean “no assumptions.” For example, the two-sample Wilcoxon rank-sum framework described by Hollander et al. begins with independent random samples from continuous distributions; under its distribution-free null hypothesis, the two samples share an unspecified common distribution (Hollander et al., 2014).

Interpretation matters as well. Altman notes that a confidence interval for a difference between medians based on a shift model requires the restrictive assumption that the two population distributions have identical shapes and differ only in location (Altman, 1991).

Step 6: Check Assumptions Before Running the Test

Statistical assumptions are part of the method, not an optional diagnostic performed after obtaining the P value.

Depending on the candidate procedure, relevant checks can include:

Independence or dependence structure

Are observations genuinely independent? Are there pairs, matched sets, repeated observations, clusters, or other dependencies?

This question comes first because using the wrong independence structure changes the analysis itself.

Distributional assumptions

For normal-theory methods, consider whether their distributional assumptions are reasonable for the quantity being modeled. In paired analysis, for example, the relevant distribution is typically the distribution of the within-pair differences, not simply the marginal distribution of each measurement separately.

Variance assumptions

Different versions of the independent two-sample t procedure make different variance assumptions. Rosner explicitly distinguishes equal-variance and unequal-variance two-sample t procedures (Rosner, 2016).

Outliers and influential observations

Outlying observations can strongly affect methods based directly on means and variances. Hollander et al. identify relative insensitivity to outlying observations as one advantage of many nonparametric procedures (Hollander et al., 2014).

Adequacy of large-sample approximations

Categorical-data chi-square procedures use large-sample approximations. When data are sparse, exact methods may need consideration. Rosner’s inference flowchart, for example, distinguishes large-sample categorical procedures from Fisher’s exact test based partly on expected cell counts (Rosner, 2016).

Censoring for time-to-event outcomes

For survival outcomes, whether and how observations are censored is integral to method selection rather than a nuisance to ignore (Altman, 1991).

Step 7: For Categorical Data, Match the Method to Both Categories and Dependence

Categorical outcomes deserve their own decision branch because the appropriate method depends on more than the fact that the outcome consists of categories.

For independent categorical observations, two-way contingency-table methods can test independence or compare proportions. Agresti discusses Pearson chi-square and related inference for two-way contingency tables, as well as the limitations of chi-square approximations in sparse settings (Agresti, 2013).

For paired binary data, ordinary independent-sample contingency-table analysis fails to use the matched structure. McNemar methods instead target binary matched pairs (Agresti, 2013; Rosner, 2016).

For multicategory outcomes, the distinction between nominal and ordinal categories becomes important. Agresti uses baseline-category logits for nominal responses and cumulative-logit approaches for ordinal responses, illustrating why category ordering should influence model selection (Agresti, 2013).

Categorical data also illustrate why software menus are a poor starting point. “Chi-square,” “logistic regression,” “McNemar,” and “multinomial regression” can all involve categorical variables, but they answer different questions under different data structures.

Step 8: Do Not Let Hypothesis Testing Replace Estimation

Choosing the correct test is only part of a good analysis. Researchers generally need to know how large the observed difference or association is and how precisely it has been estimated, not merely whether a null hypothesis was rejected.

Altman emphasizes that a confidence interval communicates the uncertainty or lack of precision in an estimate and therefore provides information beyond a dichotomized significance decision. He recommends presenting confidence intervals and, where useful, actual P values rather than allowing the P value to carry the entire interpretation (Altman, 1991).

This changes how to think about the question “Which statistical test should I use?”

That perspective prevents statistical significance from being mistaken for scientific or clinical importance.

When a Simple Statistical Test Is No Longer Enough

A one-test decision table is most useful for relatively simple unadjusted questions. Many health-research analyses require a model instead.

Consider moving beyond a simple group-comparison test when:

• several predictors or covariates must be considered simultaneously;
• adjustment for other variables is required;
• the outcome is binary, nominal, or ordinal and regression effects are of interest;
• observations have complex dependence structures;
• event times and censoring matter; or
• the research question concerns relationships among several variables rather than a single unadjusted comparison.

Rosner’s inference flowchart explicitly moves from simple comparisons to multiple regression, multiple logistic regression, and survival-analysis methods as the structure of the question becomes more complex (Rosner, 2016).

Common Mistakes When Choosing a Statistical Test

A Reusable Statistical Test Selection Checklist

Before finalizing an analysis, confirm that you can answer each of these questions:

• What is the primary research question?
• What quantity or contrast do I want to estimate?
• What type of outcome do I have: continuous, binary, nominal, ordinal, count/rate, or time-to-event?
• Are the observations independent, paired, matched, repeated, or otherwise dependent?
• Am I dealing with one, two, or more than two groups or conditions?
• Does censoring matter?
• Do I need an unadjusted comparison or a regression/modeling approach?
• What assumptions does the candidate method require?
• Have I examined whether those assumptions are reasonable?
• If choosing a nonparametric method, do I understand what hypothesis and estimand it actually addresses?
• What effect estimate should I report?
• What confidence interval or other uncertainty measure should accompany it?
• Does the interpretation distinguish statistical evidence from the magnitude and practical importance of the result?

If several of these questions cannot be answered, the analysis is not yet at the “pick a test” stage.

Bottom Line

When asking “Which statistical test should I use?”, do not begin by choosing between a t test, chi-square test, ANOVA, or Mann–Whitney test.

For continuous outcomes, that process may lead to t tests, ANOVA, or rank-based procedures. For categorical outcomes, it may lead to contingency-table methods, McNemar analysis, or categorical regression. For time-to-event outcomes, censoring leads to survival-analysis methods. Paired and repeated observations must be distinguished from independent samples throughout.

Most importantly, choosing a statistical test is not a software-selection exercise. It is a translation from the scientific question and study design into an inferential problem.

Frequently Asked Questions

1. Which statistical test should I use for two groups?

First determine whether the outcome is continuous, categorical, ordinal, or time-to-event and whether the groups are independent or paired. Two independent groups with a continuous outcome may lead to a two-sample t test or an appropriate rank-based method; paired continuous observations instead lead to paired procedures. Binary outcomes require categorical methods rather than continuous-outcome tests (Altman, 1991; Rosner, 2016).

2. How do I choose between a paired and independent statistical test?

Base the decision on the design, not on the observed values. If observations are linked through repeated measurements, matching, or another pair structure, use a method that incorporates that dependence. If the samples consist of unrelated observational units, an independent-samples method may be appropriate (Altman, 1991).

3. When should I use a parametric vs nonparametric test?

Do not decide from a normality test alone. Consider the research question, target of inference, sample structure, distributional assumptions, outliers, and assumptions of the proposed rank-based procedure. Nonparametric methods often require fewer distributional assumptions, but they are not assumption-free (Hollander et al., 2014).

4. What statistical test should I use for categorical data?

It depends on the categories and design. Independent categorical observations may lead to contingency-table methods; sparse tables may require alternatives to large-sample chi-square approximations. Paired binary responses require matched-pair methods such as McNemar analysis. Regression with binary, nominal, or ordinal outcomes requires models suited to the corresponding response structure (Agresti, 2013).

5. What test should I use for more than two groups?

For an appropriate continuous outcome with independent groups, one-way ANOVA is a standard candidate. A Kruskal–Wallis procedure provides a rank-based method for a one-way independent-samples layout. Related or repeated conditions require methods that preserve the dependence structure rather than treating all observations as independent (Rosner, 2016; Hollander et al., 2014).

6. Is Mann–Whitney simply the nonparametric version of the independent t test?

It is a common rank-based alternative for two independent groups, but the two procedures should not be assumed to estimate or test exactly the same feature under all distributions. Interpretation of Mann–Whitney as a pure location or median comparison requires additional assumptions about the population distributions (Altman, 1991; Hollander et al., 2014).

7. Should I report a P value or a confidence interval?

Where an effect can be meaningfully estimated, report the effect estimate with its confidence interval; an actual P value may also be useful. Altman emphasizes that confidence intervals convey the precision and uncertainty of an estimate and therefore provide information that a significance decision alone does not (Altman, 1991).

References

Agresti, A. (2013). Categorical data analysis (3rd ed.). Wiley.

Altman, D. G. (1991). Practical statistics for medical research. Chapman & Hall.

Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric statistical methods (3rd ed.). Wiley.

Rosner, B. (2016). Fundamentals of biostatistics (8th ed.). Cengage Learning.