Which Statistical Test to Use in Medical Research? A Practical Guide

Introduction

There is a specific moment in every research where things get really crucial. Data collection is finished, the spreadsheet is clean, and you sit down to actually analyze it. Then nothing happens, because you have no idea which statistical test to use in medical research when your data look like this.

T-test or Mann-Whitney U. ANOVA or Kruskal-Wallis. Chi-square or Fisher’s exact. The names all sound similar and half the online explanations contradict each other.Most of us get past this by scrolling through a published paper with a similar design and borrowing whatever they used. It usually works but it can fall apart the moment a supervisor or reviewer asks you to justify the choice.

The honest answer is that four things drive the decision. Your outcome variable, your grouping or predictor variable, the number of groups or measurements involved, and whether the observations are independent or paired. That is it. Everything below builds on those four.

How to Choose a Statistical Test in Medical Research

Don’t guess which statistical test to use in your research. Follow a proper pattern till you make that decision. If you follow the steps given below, there are high chances that you will be able to decide which test will give best results for your research. 

Step 1: What is your outcome variable?

Is it a number or a category? Systolic blood pressure, HbA1c, hemoglobin, length of stay, all numbers. Those lead you to t-tests, ANOVA, correlation, and linear regression. Alive or dead, responder or non-responder, complication or none, those are categories, and they point to chi-square, Fisher’s exact, and logistic regression. Answering this alone removes about half the options.

Outcome type is decided at the design stage, not the analysis stage. Study design in medical research covers how each design shapes what your outcome variable can be.

Step 2: How many groups?

One, two, or three and above. Sounds obvious. It still causes trouble, because people compare four treatment arms with a series of t-tests instead of moving up to ANOVA.

Step 3: Independent or paired?

Independent means every value came from a different patient. Paired means the same patient gave you two values, or you matched patients to each other deliberately.

Step 4: Do the parametric assumptions actually hold?

Roughly normal distribution, a measurement scale that suits the test, genuinely independent observations, and reasonably similar variances between groups when the test needs that.

Once you have all four answers, you are usually choosing between one or two tests rather than staring at a list of twenty.

If terms like distribution, variance and independence are still doing a lot of unexplained work here, medical statistics for beginners covers each one before you need to judge whether an assumption holds.

Parametric vs Non-Parametric Tests

Parametric tests come with strings attached. They assume your data follow a particular distribution, almost always something close to normal, and in exchange they give you good statistical power. T-test, ANOVA, Pearson correlation. This is the family of tests that most medical papers use.

Non-parametric tests ask for much less. Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Spearman correlation. They are what you reach for with badly skewed data, ordinal outcomes like pain scores or NYHA class, and certain small samples.

The rule that most people follow: Normal data, use parametric. Non-normal data, use non-parametric.

It is not exactly wrong. It is just far too thin to carry the whole decision. Study design matters. So does whether your outcome is ordinal or truly continuous, whether your observations are independent, how large your sample is, and what question you set out to answer in the first place. Normality feeds into the choice. It does not make the choice for you.

t-Test vs Mann-Whitney U Test

Question	t-test	Mann-Whitney U
Outcome	Continuous	Continuous or ordinal
Groups	2	2
Data	Approximately normal	Non-normal or ordinal
Design	Independent or paired version	Independent

Independent t-test:  Two separate groups of patients, and you want to compare their means. Mean systolic blood pressure in patients on Drug A against patients on Drug B. Different people in each group.

Paired t-test: Same patients, measured twice. You record blood pressure before treatment, then again eight weeks later in those same patients.

Mann-Whitney U: Two independent groups again, but this time the t-test assumptions are shaky or your outcome is ordinal.

Before you go further, there is one mistake worth killing off now, because it turns up in submitted manuscripts all the time. The non-parametric test for paired data is Wilcoxon signed-rank. Not Mann-Whitney. If you run Mann-Whitney on before-and-after data, you have thrown away the pairing, which was the entire point of measuring the same patients twice.

ANOVA vs Kruskal-Wallis Test

Three or more independent groups, and you want to know whether they differ. Both of these answer that.

One-way ANOVA: It works for a continuous outcome across three or more independent groups, provided the assumptions are holding up. Mean HbA1c across three treatment arms is the textbook case.

Kruskal-Wallis: Covers you when the outcome is ordinal, or when the distribution makes a non-parametric approach the safer call.

Here is the part that gets skipped. Your ANOVA comes back significant, you write “the groups differed significantly,” and you move on. But a significant ANOVA only says the groups are not all identical. It does not say which one is different. Group A could differ from group C while B sits in the middle, and the omnibus test will not tell you that.

You need post-hoc testing to find out. Tukey’s test is the common choice, partly because it adjusts for the fact that you are now running multiple comparisons.

Kruskal-Wallis behaves the same way. Significant result, then appropriate pairwise comparisons, then you can name the groups that differ.

Chi-Square vs Fisher’s Exact Test

Two categorical variables, and you want to know if they are associated.

Chi-square: It is the standard option when the expected counts in your table are big enough. Is smoking status associated with myocardial infarction across 400 patients? Chi-square handles that comfortably.

Fisher’s exact test: It covers you when the numbers get thin. A small trial where only five patients had the outcome, for example, leaves cells too sparse for chi-square to behave.

There is a shortcut floating around the internet that says use Fisher’s when n is under 20. Ignore it. The number that matters is the expected cell count, not your total sample size. You can enroll 200 patients and still end up with expected counts below five in a couple of cells, simply because the outcome is rare. SPSS prints the expected counts for you. Read them before you commit.

Where to find them, and what else the output is telling you, is covered in the SPSS tutorial for medical research. STATA reports the same table under a different label.

Paired vs Unpaired Data: Why It Changes the Test

Of the four questions, this is the one people forget, and it flips the answer completely.

Unpaired data, also called independent data, come from two different sets of patients. Drug A group and Drug B group. Depending on what your outcome looks like, you are choosing between an independent t-test, Mann-Whitney U, or chi-square and Fisher’s exact.

Paired data come from the same patients at two points in time. Before and after. Those go to a paired t-test or Wilcoxon signed-rank.

Once you have three or more measurements on the same patients, say a lab value at baseline, three months, and six months, you move up again to repeated-measures ANOVA or the Friedman test.

So ask yourself one question before anything else. Did each number in this column come from a different person, or are some of them linked to each other? Get that backwards and every calculation after it is wrong, even though the software will run it happily and print a p-value.

How to Test for Normality Before Choosing a Test

Three tools, and you want all three rather than any one of them on its own.

• Histogram. The quickest look at the shape of your distribution.
• Q-Q plot. Points sitting close to the diagonal line mean you are in reasonable shape.
• Shapiro-Wilk test. The formal test that gives you a p-value.

That p-value is where people get into trouble. In a large sample, Shapiro-Wilk becomes so sensitive that it flags departures from normality too small to matter to anyone. In a very small sample it lacks the power to detect problems that genuinely do matter. Either way, letting one p-value decide your entire analysis strategy is not a great plan.

Look at the histogram. Check how skewed things really are. Factor in your sample size. Think about the variable itself and whether you would expect it to be normally distributed in the first place. It also helps to know that t-tests and ANOVA are more forgiving than their reputation suggests, and they tolerate moderate deviations from normality reasonably well once your sample size is decent. A slightly long tail is not a reason to abandon them.

Sample size is doing a lot of work in that judgement, and it is fixed long before you get here. Sample size calculation for clinical research covers how power and effect size determine what your data can realistically detect.

What If You Have More Than One Predictor?

Everything so far assumes one grouping variable. Clinical data are messier than that. Age, sex, BMI, diabetes, and smoking status are all sitting in your dataset influencing the outcome, and a t-test cannot account for any of them.

That is what regression is for.

Linear regression when the outcome is continuous. Predicting systolic blood pressure from age, BMI, and smoking status together.

Logistic regression when the outcome is binary. Whether a patient develops a postoperative complication, yes or no.

Multiple regression simply means you have more than one predictor in the model. The payoff is that you can look at the effect of one variable while holding the others steady, which is usually the question you actually cared about.

Statistical Test Selection Cheat Sheet

Research question	Common test
Compare two independent means	Independent t-test
Compare two paired means	Paired t-test
Two independent non-normal groups	Mann-Whitney U
Two paired non-normal measurements	Wilcoxon signed-rank
Compare 3+ independent means	One-way ANOVA
Compare 3+ non-normal independent groups	Kruskal-Wallis
Compare categorical variables	Chi-square
Small categorical sample	Fisher’s exact
Compare 3+ repeated measurements	Repeated-measures ANOVA or Friedman
Correlation between continuous variables	Pearson or Spearman
Binary outcome prediction	Logistic regression
Continuous outcome prediction	Linear regression

Save this somewhere, but treat it as a shortlist rather than a final answer. The table gets you to a candidate test. Checking your design and assumptions is what confirms it.

Common Statistical Test Selection Mistakes in Medical Research

Choosing based on sample size alone: Sample size is one input. Variable type, design, and assumptions matter just as much, and often more.

Using Mann-Whitney on paired data: Wilcoxon signed-rank is the paired non-parametric test. This one is worth double-checking every single time.

Assuming non-normal automatically means non-parametric: How badly skewed is it, really? Are the other assumptions fine? Answer those first.

Running six t-tests instead of one ANOVA: Each comparison carries its own chance of a false positive. Stack six of them and your real error rate is nowhere near the 5% you think you are working with.

Confusing statistical significance with clinical significance: A p-value of 0.03 tells you the result is unlikely under the null hypothesis. It says nothing at all about whether a 2 mmHg difference would change how you treat a patient.

Picking the test after looking at the results:  Decide first. If you choose the test that gives you the p-value you wanted, you are no longer testing anything.

Frequently Asked Questions

Q1. What statistical test is most commonly used in medical research?

There is no single answer. T-tests, chi-square, ANOVA, regression models, and the non-parametric alternatives all appear constantly, and which one shows up depends entirely on the design of the study.

Q2. How do I know whether to use a parametric or non-parametric test?

Look at your outcome type, how the data are distributed, whether your observations are independent, and whether the assumptions behind the parametric test are holding up.

Q3. What is the difference between a t-test and Mann-Whitney U test?

Both compare two groups. The t-test compares means and expects approximate normality. Mann-Whitney is the alternative when that expectation does not hold, or when your outcome is ordinal.

Q4. When should I use ANOVA instead of a t-test?

As soon as you are comparing three or more groups.

Q5. When should I use Fisher’s exact test instead of chi-square?

When the expected counts in your contingency table are small.

Q6. How do I choose a statistical test in SPSS?

The same way you would anywhere else. SPSS, R, and Stata will run whatever you click on and give you a result either way, so the reasoning has to happen before you open the software.

Key Takeaway

The sequence never really changes:

Outcome → Groups → Paired or independent → Distribution and assumptions → Research question → Statistical test

Go through it properly and the right test usually becomes obvious somewhere around step three. And resist the urge to copy a test from a published paper just because the topic looks similar. That study had its own design, its own data, and its own question. None of those are yours.

Before you start recruiting, walk through your assumptions with a statistician or an experienced researcher.If you are planning your first review and want guidance through protocol development, search strategy, screening, and manuscript preparation, the American Academy of Research and Academics offers mentorship and training for students and early career researchers. Get in touch to talk through your project and get support from your first research question through to submission.

Disclaimer:

Articles published by American Academy of Research & Academics are prepared by our team using information from direct experience, publicly available resources, and educational references. AI tools may be used to assist with drafting, proofreading, and formatting; however, all content undergoes review and approval before publication.
The information provided is intended for educational purposes only. Requirements, policies, and processes may change over time. Readers should consult official sources for the most current information.