Free tool · no sign-up
Four-fifths rule and adverse impact calculator.
Enter how many people applied and how many were selected in each group. You get each group's selection rate, its impact ratio against the highest group, whether it passes the 80% rule, and whether the difference is statistically significant.
"Selected" can be hired, promoted, approved or advanced: any favorable outcome. Significance uses a two-proportion z-test against the highest group (p < 0.05).
The calculator tells you if. A review tells you why, where and what to fix.
Eighty percent of the top rate, and why it matters.
The four-fifths rule comes from the U.S. Uniform Guidelines on Employee Selection Procedures (29 CFR 1607.4(D)). Agencies such as the EEOC have long used it as a first screen for adverse impact in hiring, promotion and other selection decisions. Read more about how it works, its limits and where it stands in 2026.
Two divisions
selection rate = selected ÷ applicants
impact ratio = group rate ÷ highest group rate
If any group's impact ratio is below 0.80, the procedure is generally regarded as having adverse impact on that group, and the employer is expected to look into it.
Men 30%, women 21%
- 480 men applied and 144 were hired: a selection rate of 30.0%
- 520 women applied and 109 were hired: a selection rate of 21.0%
- Impact ratio for women: 21.0 ÷ 30.0 = 0.70, below 0.80
- With these sample sizes the gap is also statistically significant, so it is unlikely to be chance
Passing the rule is not the same as being fair.
The four-fifths rule is a rule of thumb, not a safe harbor. These are the gaps we see most often.
Sample size
With thousands of applicants, a ratio of 0.90 can be significant. With twenty, a ratio of 0.60 can be noise. Read the ratio and the significance together.
Intersections
Women and Black applicants can each pass while Black women fail. NYC Local Law 144 requires intersectional categories for this reason.
Proxies
If you don't collect race, the rule can't be run at all, yet a model can still learn it from data that looks neutral.
Who was wrong
Equal rates can hide unequal errors: qualified people from one group rejected more often than qualified people from another.
Need more than the basic math? A Fairspire Review checks intersectional groups, proxy bias and wrongful rejections in any department, with a written report. One-off or quarterly, without sending us personal data.
About the four-fifths rule.
What is the four-fifths rule?
The four-fifths or 80% rule comes from the U.S. Uniform Guidelines on Employee Selection Procedures (29 CFR 1607.4(D)). A selection rate for any group that is less than four-fifths (80%) of the rate for the group with the highest rate is generally regarded as evidence of adverse impact.
How do you calculate adverse impact?
Compare selection rates between groups. For each group, divide the number selected by the number of applicants. Then divide each group's rate by the highest group's rate to get its impact ratio. Under the Uniform Guidelines (29 CFR 1607.4(D)), a ratio below 0.80, four-fifths, is generally regarded as evidence of adverse impact. Check statistical significance too, especially with small or very large samples.
How do you calculate the impact ratio?
Divide each group's selection rate by the highest group's selection rate. A selection rate is the number selected divided by the number of applicants in that group. A ratio below 0.80 fails the four-fifths rule of thumb.
Is passing the four-fifths rule enough?
No. It is a rule of thumb, not a legal safe harbor. Smaller differences can still be significant with large samples, large differences can be noise with small samples, and a clean result for sex or race alone can hide a problem for an intersectional group, such as Black women.
Does it only apply to hiring?
The Uniform Guidelines cover employment decisions such as hiring, promotion and termination. The same ratio is widely used as a first check for other decisions about people, such as loan approvals or tenant screening, where other laws and tests also apply.
Is this the same as a NYC Local Law 144 bias audit?
No. Local Law 144 requires an independent auditor to calculate selection or scoring rates and impact ratios by sex, race/ethnicity and intersectional category, and to publish a summary. This calculator shows the basic math only. See how Fairspire supports Local Law 144 audits.
Is my data sent anywhere?
No. The calculation runs entirely in your browser. Nothing you type is sent to Black Odix or anyone else, and nothing is stored.