Pay equity analysis is a statistics problem before it’s a policy one.
Raw pay gaps make headlines; controlled analysis makes decisions. This tool runs the comparisons properly — like-for-like groupings, explained versus unexplained variance, confidence about what the data can and cannot support — and connects findings to remediation you can budget.
Two gaps, two conversations, frequently confused
The raw gap — average pay across a whole population — measures representation as much as pay: if senior roles skew one way, the raw gap reflects that, and closing it means changing who holds which jobs. The controlled gap asks a narrower question: within comparable work, are people paid differently in ways the legitimate factors don’t explain? Both are real; they demand different remedies, and conflating them produces arguments instead of progress.
Careful analysis separates them and stays honest about limits. Comparator groups need enough people to support conclusions; some variance stays genuinely unexplained without being evidence of discrimination; and small populations often can’t support statistical claims at all. A tool that reports confidence honestly is more useful than one that produces a confident number for every cut.
Comparator groups that hold up
Grouping by role, level, location and tenure with minimum-size thresholds — and an explicit statement where a group is too small to support conclusions.
Findings that connect to the comp cycle
Identified adjustments carry costs and flow into merit planning — so equity work gets funded in the cycle rather than becoming a report nobody can action.
Privilege and process handled
Pay equity analysis often runs under legal privilege — access controls, retention and disclosure boundaries are configurable to match your counsel’s process.
The gap, found before it’s filed
Like-for-like comparisons across gender and level, with the unexplained residue isolated for action.
Interface shown as an illustration with representative numbers, not a screenshot — the layout is the product’s.
Run the controlled analysis first.
Before the headline number, get the comparator structure right — we’ll walk through it with your comp and legal leads.
The evidence this page stands on
Questions buyers ask
Does this constitute legal compliance with pay-transparency laws?
No — reporting obligations vary by jurisdiction and your counsel determines them. The tool produces the analysis; whether and how you report it is a legal decision.
How large does a comparator group need to be?
Enough to support statistical inference — commonly cited thresholds start around thirty, though it depends on effect size and variance. The tool flags groups too small rather than producing false confidence.
What if the unexplained residual is small but non-zero?
Some residual is normal in any real dataset. The useful questions are whether it’s stable across cycles, patterned by protected characteristics, and material in monetary terms — the analysis addresses all three.
Should analysis run under legal privilege?
Many organisations do so, particularly for first analyses. The platform’s access and retention controls are configurable to support that; your counsel decides the approach.
Is this product available today?
Please confirm current availability for the Total Rewards capabilities rather than assuming from this page.
See it on your own content.
Bring one course. We’ll show you the retention curve your current training leaves behind — and what scheduled review does to it.
- 30 minutes, on your calendar — pick a slot here
- Run on your own content wherever possible, not a canned deck
- You see the dashboards, the learner surface and the evidence exports
- No commitment — and pilot data stays yours either way