Use the fields below to compute pass rate and related hiring funnel signals. Results appear above after you submit.
Sample role-level snapshot to illustrate common reporting fields.
| Role | Applicants | Screened | Passed | Pass rate | Coverage |
|---|---|---|---|---|---|
| Customer Support Associate | 240 | 180 | 54 | 30.00% | 75.00% |
| Marketing Analyst | 120 | 90 | 18 | 20.00% | 75.00% |
| Backend Developer | 180 | 120 | 28 | 23.33% | 66.67% |
| Product Designer | 95 | 70 | 14 | 20.00% | 73.68% |
| Data Engineer | 110 | 80 | 16 | 20.00% | 72.73% |
- Pass rate (%): passed ÷ denominator × 100
- Fail rate (%): failed ÷ denominator × 100
- Coverage (%): screened ÷ total applicants × 100
- Overall yield (%): passed ÷ total applicants × 100
- Denominator: screened (standard), or screened + withdrawn if you include withdrawals.
- Wilson interval: Uses your confidence selection to estimate a stable range for pass rate, especially useful when volumes are small.
Interpretation tip: very low pass rates can indicate overly strict criteria or poor sourcing fit; very high rates can indicate loose filters, strong sourcing, or unclear role requirements.
- Enter your total applicants and the number screened for the period.
- Enter how many candidates passed screening; optionally enter failed.
- Add withdrawals if you track them, then choose the denominator rule.
- Optionally set a target, baseline, and an expected min/max range.
- Press Calculate to view results above the form.
- Use CSV/PDF exports for audit trails and stakeholder updates.
For consistent reporting, keep definitions stable (what counts as “screened” and “passed”) and review outliers by role, source, and recruiter.
Screening pass rate as an early quality signal
Screening pass rate quantifies how many screened candidates advance, helping teams validate sourcing fit and screening criteria. A stable baseline often emerges after 8–12 weeks of consistent role definitions and interviewer calibration. For high‑volume roles, 20–35% is common; for senior roles, 10–20% is typical. Use the period, department, and role fields so comparisons stay consistent across hiring cycles.
Coverage and yield reveal funnel friction
Coverage (screened ÷ applicants) highlights operational throughput. If coverage is low, backlogs can inflate time‑to‑respond and distort pass rate because only a subset is evaluated. Overall yield (passed ÷ applicants) complements pass rate by showing top‑of‑funnel efficiency. Track both weekly; a 10‑point drop in coverage usually signals capacity constraints or unclear intake. Pair this with a first‑review SLA to interpret workflow changes and reduce decision-cycle variance overall.
Denominator choices and withdrawal handling
Standard reporting uses screened candidates as the denominator, matching most ATS dashboards. If withdrawals are frequent, adding withdrawn to the denominator better reflects candidate flow and reduces optimism bias. Track withdrawals separately to detect compensation gaps, slow scheduling, or competing offers. A rising withdrawal share above 5–8% is often an early warning for candidate experience issues. Document your definitions so month‑to‑month reporting remains comparable.
Targets, ranges, and statistical stability
Targets work best when paired with an expected range, such as 15–35%, reflecting role seniority and labor market tightness. The Wilson confidence interval provides a stability band for small samples; wide intervals mean the rate can swing with only a few decisions. Avoid hard conclusions under 20 decisions. Choose 95% for routine reporting; use 99% for compliance. Re‑check insights once decisions exceed 30–50 and the interval narrows.
Operational actions from the metrics
When pass rate is below target with high coverage, refine job requirements, calibrate screen questions, and audit rejection reasons for consistency. When pass rate is high but overall yield is low, improve applicant quality through channels, outreach, and referral programs. Use deltas versus baseline to validate changes. Share CSV/PDF exports in weekly reviews to align recruiters, coordinators, and hiring managers.
What is considered a “screened” candidate?
Use the first stage where a pass or fail decision is recorded, such as resume screen, recruiter call, or phone screen. Keep the definition consistent across periods to maintain comparable rates.
Should withdrawals be included in the denominator?
If many candidates exit before a decision, including withdrawals can reflect true funnel leakage and reduce optimistic pass rates. If withdrawals are rare, the standard screened-only denominator is usually sufficient.
Why can pass rate improve while overall yield stays flat?
Pass rate measures efficiency within screened candidates, while overall yield reflects applicant quality and volume. Better screening can raise pass rate, but weak sourcing or low coverage can keep yield unchanged.
How much data is enough to trust the trend?
Small samples swing easily. Use the confidence interval and avoid strong conclusions under about 20 screening decisions. Trends become more stable once you have roughly 30–50 decisions for a role and period.
How do I set a target and expected range?
Start with a baseline from recent hiring cycles for the same role, then adjust for seniority and market conditions. Set a target and a realistic range (for example 15–35%) to guide calibration discussions.
Can I report this across multiple roles or teams?
Run the calculator per role or per department so denominators stay clean. Then combine the exported CSVs in a spreadsheet for leadership dashboards, keeping time period and definitions consistent.
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