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Analysis of Covariance (ANCOVA) In Minutes

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Sample Output

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How it works

Analysis of Covariance (ANCOVA) tests group differences in an outcome variable while statistically controlling for a numeric covariate, yielding adjusted group means and partial eta-squared effect size estimates.

Use when comparing groups on an outcome while controlling for a pre-existing numeric variable that may confound the group comparison.

Do not use if the covariate interacts differently with the outcome across groups (violated homogeneity of slopes), or if the covariate was measured after treatment assignment.

Built for: Research scientists, educational researchers, clinical trial statisticians, behavioral scientists, UX researchers

Typical data source: Dataset with a continuous outcome variable, a categorical grouping variable (treatment vs. control or multiple conditions), and one or more continuous covariates such as pre-test scores, age, or baseline measurements

healthcareeducationpsychologymarket research

What data do you need?

Dataset with 3 columns

group (categorical) outcome (numeric) covariate (numeric)

Minimum 30 rows

What's in the report?

Test group differences in writing scores while controlling for math score as a covariate. Compare test prep course effect adjusted for prior ability.

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Descriptive Statistics by Group

Descriptive statistics for each group showing raw means and standard deviations for both outcome and covariate variables

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ANCOVA Results

ANCOVA results table showing F-statistics, p-values, and partial eta-squared for each predictor after controlling for covariates

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Effect Sizes (Partial η²)

Visual comparison of partial eta-squared effect sizes for covariate vs group predictor

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Adjusted Group Means (LS Means ± 95% CI)

Adjusted group means with confidence intervals after controlling for the covariate

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Covariate vs Outcome by Group

Scatter plot showing covariate-outcome relationship by group to verify parallel slopes assumption

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Unadjusted vs Adjusted Group Means

Side-by-side comparison of raw vs covariate-adjusted group means

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Residuals Distribution

Distribution of ANCOVA model residuals to check normality assumption

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AI Insights

Plain-English interpretation — what the numbers mean, what's significant, and what to do next.

Related tools

Need something simpler? Tf038 Live Ttest — When you have only two groups and no continuous covariate to control for - just need a direct group mean comparison

Similar: Kruskal Wallis Test, Holm Bonferroni

The Question This Answers

Test Prep Course Effectiveness

Compare writing score outcomes between students who took a test prep course versus those who did not, controlling for prior math ability as a proxy for general academic aptitude

Questions?

See our support page for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.

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