Quick answer
Use ANOVA when your primary predictor is categorical and you are comparing mean outcomes across groups. Use regression when predictors are continuous, mixed, or multivariable. In both methods, verify assumptions, inspect residuals, and report effect sizes for meaningful interpretation.
Key takeaways
- ANOVA and ordinary linear regression are expressions of the same general linear-model framework.
- Choose the model from the research question and variable structure, not from the preferred menu in software.
- Planned contrasts often answer the research question better than an omnibus F-test alone.
- Residual diagnostics and influential observations matter in both approaches.
Model Intent Matters
ANOVA focuses on group mean differences, while regression emphasizes predictive relationships and adjusted effects across multiple variables.
Assumption Checks
Both methods require independence and variance diagnostics. Regression additionally needs linearity assessment for continuous predictors.
Unified Interpretation
ANOVA and linear regression share mathematical foundations. With coded factors, ANOVA can be represented in a regression framework.
Practical method
Step-by-Step Workflow
- 1
State the estimand
Decide whether you need group mean differences, a trend per unit of exposure, an adjusted effect, an interaction, or a prediction.
- 2
Represent predictors correctly
Code nominal groups as factors, retain meaningful continuous information, and choose a scientifically defensible reference group.
- 3
Fit the simplest adequate model
Include covariates or interactions only when they answer the question or address the design; avoid automatic stepwise selection.
- 4
Check and report
Inspect residual patterns, variance, influence, and functional form; then report estimates, contrasts, intervals, and model limitations.
Worked example
Three treatments with baseline adjustment
- Scenario
- Researchers compare mean follow-up symptom scores among three treatments and also measured each participant's baseline score.
- Approach
- Fit an ANCOVA model with treatment as a categorical predictor and baseline score as a covariate. Use planned pairwise contrasts with a stated multiplicity adjustment.
- Interpretation
- The treatment coefficients are adjusted mean differences relative to the reference treatment. The omnibus test answers whether any adjusted means differ; the contrasts identify where the differences lie.
Common Mistakes to Avoid
- Converting a continuous predictor into arbitrary categories
- Reporting only the omnibus test
- Ignoring unequal variance or nonlinearity
- Including an interaction but interpreting only main effects
Frequently Asked Questions
Can ANOVA include covariates?
Yes. ANCOVA extends ANOVA by adjusting group comparisons for continuous covariates.
Is regression always better than ANOVA?
Not always. Method choice should follow your research question, variable structure, and interpretability needs.