Q: How should I handle outliers detected by the calculator?

Review each outlier individually: 1) Verify salary data is accurate (no data entry errors), 2) Check if exceptional performance or unique skills justify premium pay, 3) Confirm job title and level are correct (misclassification can cause outliers), 4) Review tenure and experience not captured in the data, 5) If underpayment is identified without justification, develop correction plan, 6) Document review and decision for each outlier. Outliers aren't automatically problematic, but each deserves scrutiny.

Q: Can I use this for ethnicity or department comparisons, not just gender?

This simplified version focuses on gender analysis for demonstration. A full production version would support ethnicity, department, job level, and location comparisons. The same statistical principles apply: calculate unadjusted gaps, adjust for legitimate factors, test statistical significance, identify outliers. For ethnicity analysis, you may need to compare multiple groups simultaneously (ANOVA instead of t-test), which is more complex.

Q: What should I do if I find a significant pay gap?

Don't panic, but do act: 1) First, verify data accuracy and completeness, 2) Investigate whether legitimate factors explain the gap (experience, performance, role complexity), 3) Consult with employment law attorney BEFORE making widespread changes, 4) If gap is unjustified, develop phased correction plan (sudden large raises may raise red flags), 5) Document your review process and rationale, 6) Make corrections over 1-3 years to align with budget cycles, 7) Implement annual pay equity reviews going forward to prevent recurrence.

Question 1

Is this calculator a legal determination of pay discrimination?

Accepted Answer

No. This calculator provides statistical analysis for internal review and is NOT a legal determination of discrimination. Statistical disparities may result from legitimate factors not captured in this simplified analysis. You must consult with employment law attorneys and HR professionals before taking action based on these results. This tool does not constitute legal advice or compliance certification.

Question 2

What sample size do I need for reliable results?

Accepted Answer

Minimum 10 employees total, but ideally 30+ for robust statistical analysis. For group comparisons (e.g., male vs female), each group should have at least 5-10 employees. Smaller samples can produce misleading results due to random variation. Larger samples (50+) provide more statistically reliable conclusions. Very small companies may not have sufficient data for meaningful statistical analysis.

Question 3

What does 'statistically significant' mean?

Accepted Answer

Statistical significance (p < 0.05) means there's less than 5% probability that the observed pay gap is due to random chance. In other words, 95%+ confidence that a real disparity exists. However, statistical significance doesn't prove discrimination - it just means the difference is unlikely to be coincidental. You must still investigate whether legitimate factors explain the gap.

Question 4

How should I handle outliers detected by the calculator?

Accepted Answer

Review each outlier individually: 1) Verify salary data is accurate (no data entry errors), 2) Check if exceptional performance or unique skills justify premium pay, 3) Confirm job title and level are correct (misclassification can cause outliers), 4) Review tenure and experience not captured in the data, 5) If underpayment is identified without justification, develop correction plan, 6) Document review and decision for each outlier. Outliers aren't automatically problematic, but each deserves scrutiny.

Question 5

Can I use this for ethnicity or department comparisons, not just gender?

Accepted Answer

This simplified version focuses on gender analysis for demonstration. A full production version would support ethnicity, department, job level, and location comparisons. The same statistical principles apply: calculate unadjusted gaps, adjust for legitimate factors, test statistical significance, identify outliers. For ethnicity analysis, you may need to compare multiple groups simultaneously (ANOVA instead of t-test), which is more complex.

Question 6

What should I do if I find a significant pay gap?

Accepted Answer

Don't panic, but do act: 1) First, verify data accuracy and completeness, 2) Investigate whether legitimate factors explain the gap (experience, performance, role complexity), 3) Consult with employment law attorney BEFORE making widespread changes, 4) If gap is unjustified, develop phased correction plan (sudden large raises may raise red flags), 5) Document your review process and rationale, 6) Make corrections over 1-3 years to align with budget cycles, 7) Implement annual pay equity reviews going forward to prevent recurrence.

Pay Equity Analyzer

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Pay Equity Analysis

Understanding Pay Equity Analysis

1. Legal & Compliance Context

2. Unadjusted vs Adjusted Pay Gaps

3. Statistical Significance

4. Outlier Detection

5. Legitimate Pay Differences

6. Compliance Risk Assessment

7. Taking Action on Results

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