Research Guide

How to Choose the Right Statistical Test

A practical framework for picking the right statistical test: start from your research question, data type and study design, then check the assumptions.

The hardest part of a statistical analysis is often the first decision: which test to use. There is no single correct answer for every study, but there is a reliable decision path. Work through four questions in order — your research goal, your data type, your study design, and your assumptions — and most analyses reduce to a small set of well-understood tests.

The PanelRoster Statistical Test Selector automates exactly this decision path. This guide explains the reasoning behind it so you can apply the same logic by hand and judge whether a recommended test is right for your situation.

Step 1: State the research question

Every test answers a specific kind of question. Ask which of these you are trying to answer: comparing one group to a known value, comparing two groups, comparing three or more groups, comparing proportions, testing the association between two variables, or predicting an outcome from one or more predictors.

The same data can answer different questions with different tests, so write the question down before choosing the test.

Step 2: Identify the data type

Continuous data are measured on a scale with meaningful spacing (age, income, time, a 0–10 rating). Binary data have exactly two outcomes (yes/no, converted/not). Categorical data have named categories with no natural order (region, brand). Ordinal data have ordered categories where the gaps are not equal (agree/neutral/disagree, rank).

Data type drives the test family. Continuous outcomes usually point to t-tests, ANOVA, correlation or regression; counts point to chi-square; binary outcomes point to proportion tests or logistic regression.

Step 3: Determine the design

Are the observations independent, or are they paired or repeated? Independent samples come from separate units (two groups of different people). Paired data come from the same units measured twice (before/after, or matched pairs). This distinction changes the test: independent groups use an independent-samples t-test, while related measurements use a paired t-test.

Step 4: Match the test

The common cases are: one continuous sample against a known value uses a one-sample t-test (or a z-test when the population standard deviation is known); two independent continuous groups use Welch's t-test; two paired continuous measurements use a paired t-test; three or more independent continuous groups use a one-way ANOVA; categorical counts in two or more groups use a chi-square test of independence; the linear association between two continuous variables uses Pearson correlation; predicting a continuous outcome uses linear regression; and comparing proportions across groups uses a z-test for proportions or a chi-square test.

Step 5: Check the assumptions

Every test carries assumptions. Common ones are approximate normality of the data (or a large enough sample for the central limit theorem), independence of observations, roughly equal variances between groups for a pooled test, and expected cell counts of at least 5 for chi-square tests. Welch's t-test and the Kruskal-Wallis test are safer defaults when variance or normality assumptions are doubtful.

A significant p-value from a test whose assumptions are badly violated is not trustworthy. The tests the PanelRoster calculators implement each state their assumptions on their tool pages.

Key takeaways

Choose the test from the research question, the data type and the design — in that order.
Two independent continuous groups: Welch's t-test. Paired measurements: paired t-test. Three or more groups: one-way ANOVA.
Counts in categories: chi-square. Two continuous variables: correlation. Predicting an outcome: regression.
Check assumptions before trusting any p-value.

Tools used in this guide

References

References are provided for further reading; PanelRoster is not affiliated with the linked resources.