Statistical Tests Explained Simply: When to Use What

July 26, 2026

You ran a survey. You have sales data. You want to know if a change actually made a difference. But which statistical test do you use? Here's a plain-English guide to the most common tests, when to use them, and how to interpret the results.

The Core Idea

Statistical tests answer one question: is what I'm seeing real, or could it have happened by chance? They do this by calculating a p-value. If the p-value is below 0.05 (the standard threshold), the result is "statistically significant" which likely didn't happen by random chance.

t-Test: Comparing Two Groups

Use when: You want to compare the average of two groups.

What you need: One categorical variable (group A vs group B) and one continuous variable (the thing you're measuring).

Result: If p < 0.05, the two groups are significantly different. The test also tells you the effect size (how big the difference is).

ANOVA: Comparing Multiple Groups

Use when: You want to compare averages across three or more groups.

Why not multiple t-tests: Running t-tests on every pair increases the chance of a false positive. ANOVA handles multiple groups in one test.

Result: If p < 0.05, at least one group is different from the others. Post-hoc tests tell you which specific groups differ.

Chi-Square: Testing Categories

Use when: You want to know if two categorical variables are related.

What you need: Two categorical variables with counts or frequencies.

Result: If p < 0.05, the two variables are not independent. There's a relationship between them.

Correlation: Measuring Relationships

Use when: You want to know if two continuous variables move together.

What you get: A correlation coefficient from -1 to 1. Close to 1 means strong positive relationship. Close to -1 means strong negative relationship. Close to 0 means no relationship.

Important: Correlation does not mean causation. Two things can be correlated without one causing the other.

Regression: Predicting Outcomes

Use when: You want to predict one variable from others, or understand how variables influence an outcome.

What you get: An equation that shows how each predictor influences the outcome, with statistical significance for each factor.

Let AI Pick the Right Test

You don't need to memorize these rules. Queryly's Smart Test Selector analyzes your data and automatically recommends the right statistical test. Upload your file, describe what you want to compare, and the AI handles the rest.

  1. Go to getqueryly.com
  2. Upload your CSV or Excel file
  3. Click "Smart Stats"
  4. Describe what you want to test
  5. Get the test results with interpretation
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