Group 1
Enter annual salaries separated by commas. Minimum 2 values.
Group 2
Your Results
Enter compensation data for two groups to analyze.
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What This Calculator Does
This DEI pay equity analyzer compares compensation data between two employee groups to identify potential pay gaps. It calculates mean and median compensation for each group, the absolute and percentage pay gap, Cohen d effect size (a standardized measure of gap magnitude), approximate statistical significance using a two-sample t-test, and the estimated cost to bring the lower-paid group to parity. The tool helps HR teams, compensation analysts, and DEI professionals conduct preliminary pay equity analyses with 2026 benchmarks and regulatory context.
The Formula
The mean gap is the simple difference in average compensation between two groups. The percentage gap expresses this as a proportion of the higher-paid group average. Cohen d is a standardized effect size that accounts for variability within the groups. A Cohen d below 0.2 is negligible, 0.2-0.5 is small, 0.5-0.8 is medium, and above 0.8 is large. Statistical significance is approximated using a two-sample t-test comparing the means relative to the standard error. The remediation cost estimates what it would cost to raise every employee in the lower-paid group to parity with the higher group mean.
Step-by-Step Example
Enter Group 1 data
Label: Male. Salaries: $85K, $92K, $78K, $88K, $95K, $82K, $90K, $87K, $91K, $84K.
Enter Group 2 data
Label: Female. Salaries: $80K, $86K, $75K, $82K, $88K, $77K, $84K, $81K, $85K, $79K.
Review gap analysis
Male mean: $87,200. Female mean: $81,700. Gap: $5,500 (6.3%). Cohen d: 1.12 (large). Statistically significant.
Estimate remediation
To bring 10 female employees to male mean: $55,000 total remediation cost.
Real-World Use Cases
Annual Pay Equity Audit
Conduct regular pay equity analyses by gender, race/ethnicity, or other protected characteristics as required by EEOC guidance and state pay equity laws.
Remediation Budget Planning
Estimate the cost of closing identified pay gaps to build a budget request for executive approval before the next compensation cycle.
Pay Transparency Preparation
Proactively identify and address pay gaps before they become visible under expanding pay transparency laws that allow employees to discuss and compare salaries.
Common Mistakes to Avoid
Comparing raw pay without controlling for legitimate factors. Job level, experience, performance, location, and education all affect pay. An unadjusted gap does not necessarily indicate discrimination. Use this tool for initial screening, then conduct regression analysis for controlled comparisons.
Using sample sizes that are too small. With fewer than 5 employees in a group, statistical tests lack power and results may be misleading. Group employees appropriately to achieve meaningful sample sizes.
Only analyzing binary comparisons. Pay equity analysis should examine intersectional groups (e.g., Black women vs. White men at the same level) because aggregated analyses can mask disparities within subgroups.
Performing the analysis once and not repeating it. Pay equity is not a one-time exercise. Gaps can re-emerge with new hires, promotions, and market adjustments. Best practice is to analyze at least annually.
Frequently Asked Questions
Accuracy and Disclaimer
This calculator provides a preliminary pay equity screening tool. It does not control for legitimate pay-determining factors such as job level, experience, performance, education, or location. A comprehensive pay equity analysis requires multivariate regression analysis conducted by qualified compensation or statistical professionals. Results should not be used as legal evidence. Consult employment counsel before making remediation decisions based on pay equity findings.
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