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Analyze another fileTests whether pay differs significantly between demographic groups after controlling for legitimate factors like job level, department, and experience. Produces both the raw pay gap (before controls) and the adjusted gap (after controls) with statistical significance.
Use this when you have employee compensation data with demographic groups and want to check for pay equity gaps that aren't explained by role or experience.
If you're comparing performance ratings (not pay), use ANOVA. If you have more than 2 groups and multiple covariates, use ANCOVA.
Built for: HR director, compensation analyst, DE&I lead, people analytics manager
Typical data source: HRIS or payroll export with salary amounts, demographic group labels, and job-related fields (level, department, tenure)
Employee compensation data with demographic and job-level fields
Minimum 50 rows · Best with 500-5000 employees
Analyzes pay equity by comparing compensation across demographic groups (gender, ethnicity) while controlling for legitimate pay factors (role, experience, education, performance). Produces both raw and adjusted pay gap metrics with statistical significance tests.
The unadjusted pay difference between groups. A large gap here doesn't necessarily mean discrimination — it could reflect differences in role mix or seniority distribution.
Mean, median, and standard deviation of pay per group. Check if medians tell a different story than means — large discrepancies suggest outliers or skew.
The formal hypothesis test. A p-value below 0.05 means the pay difference is statistically significant — unlikely due to chance alone. Effect size tells you how large the gap is in practical terms.
This is the key number. The gap AFTER controlling for job level, department, and other legitimate factors. If this is still significant, it suggests a pay equity issue that can't be explained by role or experience.
Where the gap is concentrated. Some departments or job levels may have much larger gaps than others — these are where targeted remediation has the most impact.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? T Test — Just comparing two groups with no control variables
Need more power? Ancova — Need full ANCOVA with multiple covariates and interaction effects
Similar: Anova, Mann Whitney
See our FAQ 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.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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