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Answer

What is a good application-to-interview rate?

No published evidence supports a universal application-to-interview rate that could be called good, and many of the figures in circulation cannot be compared with one another directly.

Published rates run from about 1% to 25%. They disagree because they are measuring different things — and none of them describes the number you are trying to judge.

Published

By Maid Dizdarevic

What are we actually measuring?

Applicants invited to interview

divided by

All applications received for a posting

CareerPlug · 10M+ applications · 60,000+ US small businesses · 2023

What it found
Employers invited an average of 2% of applicants to interview, ranging from 1.2% in retail to 4.9% in education and child care.
What it does not mean
Any individual job seeker's chance of an interview, or any population beyond US small business.

Reported rates range from roughly 1.2% to 25%, but they are not competing estimates of one number. They count different events, out of different denominators, measured from different sides of hiring:

  • Employer-side funnel data counts interview invitations against every application a posting receives, whoever sent it and however well matched. One dataset of more than 10 million applications to US small businesses puts this at 2%.[1]
  • Correspondence studies send controlled fictitious applications and count any employer reply. These report roughly 7% to 25%.[2][3][4]
  • Candidate-reported surveys ask real job seekers what they sent and what came back, and publish counts rather than a rate.[5]

Your own rate is a fourth thing again: a small sample of your own search, with uncertainty attached that is usually wider than people expect.

Key findings

  1. There is no defensible universal application-to-interview benchmark.

  2. Figures that look comparable usually have different numerators, denominators, populations, and hiring stages.

  3. The widely repeated 2% benchmark could not be traced to a primary source supporting it as a general rate.

  4. Where the evidence is strongest it shows variation: a fourfold spread across industries within one dataset, and a fivefold spread across occupations within one study.

  5. An individual's observed rate carries uncertainty wide enough that comparing it to a published benchmark can mislead.

Definitions

What published numbers actually measure

Almost every dispute about this metric dissolves once you ask what is being divided by what.

An employer’s applicant tracking system counts every application that arrives, whatever its quality and whoever sent it. When that system reports a 2% rate, the denominator is that entire pile — not the subset of applications from people who actually fit the role.

A correspondence study sends a small number of carefully constructed applications, each matched to a posting the invented candidate could plausibly get. Its denominator contains no noise at all, and it usually counts any reply rather than an interview. A survey asks a person what they remember doing in the last four weeks.

These produce different numbers because they are answering different questions.

SourceExternal evidence

The same phrase, three different measurements.

CareerPlug[1]

2%

Applicants invited to interview

divided by

All applications received for a posting

10M+ applications · 60,000+ US small businesses · 2023

NotAny individual job seeker's chance of an interview, or any population beyond US small business.

Becker Friedman Institute, University of Chicago[2]

17.8%

Applications receiving any employer contact

divided by

Fictitious applications submitted by the researchers

83,000 applications · 108 large US employers · 2019–2021

NotAn interview rate: contact counts voicemails and emails of any kind, from synthetic applications.

National Bureau of Economic Research[3]

10.06%

Résumés receiving a callback

divided by

Fictitious résumés sent in response to advertisements

4,890 résumés · Boston and Chicago newspaper ads · 2001–2002

NotCurrent application volumes or channels; the study predates online application systems.

Full evidence table

All eight sources, source by source, with what each one counts and what it does not mean.

What each source counts, out of what, and what its figure does not mean. These are measurements of different quantities, not estimates of one rate.
SourceMeasurement typeNumeratorDenominatorPopulationResultWhat it does not mean
CareerPlug[1]Employer-side funnelApplicants invited to interviewAll applications receivedUS small businesses, one ATS, 20232% (1.2–4.9% by industry)Not an individual's chance of an interview: the denominator is every application the employer received, not applications from comparable candidates.
Kline, Rose & Walters[2]Audit experimentAny employer contact within 30 daysFictitious applications sentEntry-level roles, 108 large US employers, 2019–202117.8% / 15.9% by name groupContact is not an interview — mostly voicemails. The applications were synthetic and well matched.
Bertrand & Mullainathan[3]Audit experimentCallback for interviewFictitious résumés sentClerical and sales ads, Boston & Chicago, 2001–200210.06% / 6.70% by name groupNewspaper advertisements, two cities, two decades ago, before online applications existed.
Nunley et al.[4]Audit experimentAny interested reply, including requests for informationFictitious applications sentNew US college graduates, 2016–2017≈15% (5–25% by occupation)Includes requests for more information, so it is not an interview rate. A preprint.
Faberman, Mueller, Şahin & Topa[5]Candidate-reported activityInterviews recalledApplications recalledUS adults 18–64, nationally representative, 2013–2017Published as counts, not a rateNo conversion rate should be derived: the two measures come from different survey years.
Ashby[6]Employer-side, stage to stageCandidates passing a stageCandidates entering that stage54M applications, 2021–202635% at screenThe denominator is candidates already in process, roughly an order of magnitude away from a per-application rate.
What each source counts, out of what, and what its figure does not mean.These rows are not estimates of one underlying quantity. They count different events, from different sides of hiring.
Interpretation

The range 1.2% to 25% is not a benchmark range, and nothing in it identifies a true rate. Treating those endpoints as bounds on one quantity is the most common error in coverage of this question.

Source tracing

The widely repeated 2% claim

A large amount of career content states that about 2% of applicants get an interview. Jobsearch.ing could not trace that benchmark to a primary source that supports it as a general rate.

What the tracing did establish:

A 2013 recruiting-industry article describes a funnel in which, of 1,000 people who see a job post, 100 complete an application and “4 to 6 will be invited for an interview”.[7] Its own denominator is 100 completed applications — that is 4% to 6%, not 2%. The article attributes the figure to a consultancy and gives no sample size, time period, geography, or method, so it cannot establish a benchmark for anyone.

Separately, CareerPlug reports approximately 2% — but for a specific, well-defined population: applications received by US small businesses through one applicant tracking system in 2023.[1] That figure is sound for that population. It is not a universal candidate benchmark.

A frequently cited “250 résumés per corporate opening” figure is attributed to a 2015 publication that we could not retrieve: the page returned an access error, the web archive was unreachable, and a mirror of the document refused the connection. We do not know what denominator it used or whether it cited a source, so it is not treated as evidence here and no lineage between it and later 2% claims is asserted.

Interpretation

The pattern worth noticing is not who originated a number. It is that as figures travel, the population and denominator get stripped away, and a rate measured for small-business applications in one year becomes “your chance of an interview”. That happens regardless of where the number started.

Evidence

What the evidence does support

Variation — and measured within single datasets rather than assembled across incompatible ones.

Within CareerPlug’s data, using one definition, one year, one country and one business-size class, the applicant-to-interview rate varies roughly fourfold by industry.[1]

Applicant-to-interview rate by industry, US small businesses, 2023. Percentages of applications received. Source: CareerPlug per-industry benchmark pages.
IndustryInvited to interview
Retail1.2%
Automotive1.4%
Fitness1.7%
Home & commercial services2.0%
Cleaning services2.1%
Personal care2.2%
Healthcare2.6%
Restaurant & food service3.0%
Education & child care4.9%
Applicant-to-interview rate by industry, US small businesses, 2023.Percentages of applications received. Source: CareerPlug per-industry benchmark pages.

Within a single résumé audit, using one definition throughout, callback rates vary about fivefold by occupation: 5% in business and financial operations, 10% in office and administrative support, 12% in management, and 25% in sales.[4]

Identical résumés also perform differently depending on where and when they are sent. In the Boston and Chicago audit, the same résumés produced an 11.88% callback rate in Boston and 8.61% in Chicago.[3] In the larger 2019–2021 experiment, contact rates fell through late 2019, dropped during the pandemic pause, and rose sharply in the final wave — with the applications held constant throughout.[2]

Who you are affects the result. Both audits found persistent gaps by perceived race for otherwise identical applications: a callback ratio of about 1.5 to 1 in the earlier study[3], and a 2.1 percentage-point contact gap in the later one.[2]

One further finding cuts against intuition. In a nationally representative US survey, people who were employed and looking for work received the greatest number of employer contacts and interviews “despite the fact that their search effort is about half that of the unemployed”.[5] The group sending roughly twice as many applications reported fewer interviews. The survey publishes counts rather than a conversion rate, and none is derived from it here — the direction is the finding.

Interpretation

How to read your own interview rate

Your observed rate is simple arithmetic: interviews divided by applications. But that number is an estimate of an underlying process, not a measurement of it. With few applications the estimate is imprecise, and the fewer you have sent, the wider the range of underlying rates consistent with what you have seen.

observed rate = k ÷ n

k
interviews received
n
applications sent

Direct proportion

CalculationDerived by Jobsearch.ing from stated inputs

The intervals below are Jobsearch.ing calculations, not findings from any source. They use the Wilson score interval at 95% confidence, computed by the same function behind the interview rate calculator.

How precisely an observed interview rate is known, by sample size. 95% Wilson score intervals. Rounded to one decimal place. Calculated by Jobsearch.ing.
Interviews of applicationsObserved95% interval
0 of 100.0%0.0% – 27.8%
0 of 200.0%0.0% – 16.1%
0 of 300.0%0.0% – 11.4%
0 of 500.0%0.0% – 7.1%
1 of 205.0%0.9% – 23.6%
3 of 506.0%2.1% – 16.2%
2 of 1002.0%0.6% – 7.0%
5 of 1005.0%2.2% – 11.2%
10 of 2005.0%2.7% – 9.0%
How precisely an observed interview rate is known, by sample size.95% Wilson score intervals. Rounded to one decimal place. Calculated by Jobsearch.ing.

Two things follow. Twenty applications and no interviews is not, by itself, evidence that your rate is low — an underlying rate as high as 16.1% remains consistent with that observation.

And two very different rates can be indistinguishable. At 100 applications, an observed 2% gives 0.6% – 7.0% and an observed 10% gives 5.5% – 17.4%; those overlap. At 200 applications, the same comparison separates.

Check your own rate

Runs in your browser

Observed rate
5.0%
95% Wilson interval
2.2% – 11.2%
Scale adjusts to your interval
Observed rate 5.0%, with a 95% interval from 2.2% to 11.2%. The scale runs from 0% to 20% and adjusts to your interval.

This shows your observed rate and the uncertainty around it. It does not label the rate, because the evidence on this page does not support a universal benchmark to compare it against.

Open the full interview rate calculator for applications per interview and the volume tables.

Calculation

Derived by Jobsearch.ing

20
applications
0
interviews
Observed rate
0.0%
95% Wilson interval
0.0% – 16.1%

Twenty applications with no interviews gives a 95% interval running from 0% to 16.1%.

Does not establish: Does not establish that any particular rate is low, high, or typical.

Method and assumptions
Formula
centre = (p + z²/2n) / (1 + z²/n); margin = (z / (1 + z²/n)) · √(p(1−p)/n + z²/4n²)
Inputs
interviews = 0, applications = 20, z = 1.959964
Method
Wilson score interval for a binomial proportion, 95% confidence
Rounding
One decimal place
Assumes

Each application is an independent trial with the same underlying probability. Real applications share a résumé, a market and a moment, so they are not.

Calculation

Derived by Jobsearch.ing

100
applications
Observed 2%
0.6% – 7.0%
Observed 10%
5.5% – 17.4%

At 100 applications the 95% intervals around an observed 2% and an observed 10% overlap; at 200 applications they do not.

Does not establish: Specific to this pair of rates, this confidence level and these counts. It sets no general threshold.

Method and assumptions
Formula
Compare the Wilson intervals for round(0.02n)/n and round(0.10n)/n
Inputs
compared rates = 2% and 10%, application counts = 100 and 200, z = 1.959964
Method
Wilson score intervals, 95% confidence, compared for overlap
Rounding
One decimal place
Assumes

Independent trials, and interview counts rounded to whole numbers at each application count.

The width depends on how many applications you have sent, how many interviews you have had, which rate you are comparing against, and the confidence level chosen. There is no threshold at which a rate becomes meaningful; anyone quoting one is describing a decision rule they have not stated.

Interpretation

This is also why comparing your figure to a published benchmark tends not to help. At realistic application counts your interval often spans several of the figures in circulation at once. A more useful question is whether your own rate changes when you change something — a different type of role, a different channel, an application through a referral. That is a comparison within your own data, where the denominator is at least consistent.

The interview rate calculator computes this interval for your own numbers. It does not tell you whether your rate is good, because this research found no benchmark that could defend such a judgement.

Boundaries

What the evidence does not tell us

  • Whether any particular rate is good or bad. No source establishes a threshold, and none is invented here.
  • Whether interview rates have fallen over time. No located source tracks a consistent measure across years. Figures showing a decline are typically assembled from unrelated datasets with different definitions — for example a widely repeated 8.4% published without any sample size, period, or methodology.[8]
  • How much résumé tailoring changes your rate. Widely quoted improvements are not supported by any study located here.
  • How much a referral changes your rate. Referral figures in circulation measure pass-through at a later stage, among candidates already in process — 52% against 35% overall.[6] That cannot be converted into an effect on your chance of an interview from an application.
  • How rates vary by seniority. No located source breaks the metric down this way.
  • How any of this behaves outside the United States. Almost all usable evidence is US-based.

Methodology

How this was researched

Limitations

What weakens this answer

Sources

The evidence packets behind this article. Each was read directly, at the table or section named in its details.

  1. CareerPlug

    Industry report · Tier 2 · Complete population

    10M+ applications · 60,000+ US small businesses · 2023

    2024 Recruiting Metrics Report: What We Learned About Hiring in 2023

    Supports

    Employers invited an average of 2% of applicants to interview, ranging from 1.2% in retail to 4.9% in education and child care.

    Does not

    Any individual job seeker's chance of an interview, or any population beyond US small business.

    Evidence details
    Supports (full)
    Across more than 10 million applications to over 60,000 small businesses in 2023, employers invited an average of 2% of applicants to interview, ranging from 1.2% in retail to 4.9% in education and child care.
    Does not support (full)
    Does not describe any individual job seeker's chance of an interview: the denominator is every application an employer received, not applications from comparable candidates. The report describes no screening, de-duplication, or quality filtering applied to that denominator. Says nothing about large employers, professional or knowledge roles, or markets outside the United States.
    Measure
    Rate
    Counts
    Applicants invited to interview divided by All applications received for a posting
    Unit
    Applications received by an employer
    Window
    Calendar year 2023
    Population
    Applications received by small businesses using CareerPlug's applicant tracking system
    Geography
    Not stated in the report. The vendor's customer base is United States small business.
    Period
    Calendar year 2023
    Method
    Complete records of hiring activity passing through one applicant tracking system. No sampling, weighting, or exclusion criteria are disclosed beyond the volume analysed.
    Found at
    Page 8, hiring-funnel summary; per-industry benchmark pages
    Quoted
    “employers invited an average of just 2% of applicants to interview for their open roles”
    Notes
    Published by an applicant-tracking vendor, and the report also serves as marketing for that product. The page 8 summary and the per-industry pages disagree on the retail figure; the per-industry page is used here.
    Record
    Published Apr 1, 2024 · Accessed Aug 21, 2026

  2. Becker Friedman Institute, University of Chicago

    Peer-reviewed · Tier 1 · Sample

    83,000 applications · 108 large US employers · 2019–2021

    Patrick M. Kline, Evan K. Rose, Christopher R. Walters

    Systemic Discrimination Among Large U.S. Employers

    Supports

    17.8% of applications with distinctively white names and 15.9% with distinctively Black names received employer contact within 30 days.

    Does not

    An interview rate: contact counts voicemails and emails of any kind, from synthetic applications.

    Evidence details
    Supports (full)
    Across more than 83,000 fictitious applications to entry-level vacancies at 108 large United States employers, 17.8% of applications with distinctively white names and 15.9% with distinctively Black names received some form of employer contact within 30 days.
    Does not support (full)
    Contact is not an interview: it counts voicemails and emails of any kind, including requests for information. The applications were synthetic and uniformly well matched to the postings, so this does not describe what real job seekers experience. Restricted to entry-level roles at very large employers with easily audited application portals.
    Measure
    Rate
    Counts
    Applications receiving any employer contact divided by Fictitious applications submitted by the researchers
    Unit
    Researcher-submitted applications
    Window
    30 days after each application
    Population
    Fictitious entry-level applications to 108 large employers, 125 vacancies sampled per firm across distinct United States counties
    Geography
    United States, nationwide across distinct counties
    Period
    October 2019 to 2021, paused March to August 2020 for the COVID-19 pandemic; 13 months of fielded data across five waves
    Sample
    83,000 observations
    Method
    Randomised correspondence experiment. Applicant characteristics were randomly assigned across otherwise matched applications. Contact was measured as any employer contact within 30 days, predominantly by voicemail with a substantial minority by email.
    Found at
    Table 1, bottom panel, row 'Any contact in 30 days'
    Version read
    BFI Working Paper 2021-94, revised August 2021
    Notes
    Later published in the Quarterly Journal of Economics (2022). The figures here were read from the August 2021 working paper; the version of record has not been checked against them.
    Record
    Published Aug 1, 2021 · Accessed Aug 21, 2026

  3. National Bureau of Economic Research

    Peer-reviewed · Tier 1 · Sample

    4,890 résumés · Boston and Chicago newspaper ads · 2001–2002

    Marianne Bertrand, Sendhil Mullainathan

    Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination

    Supports

    Identical résumés received callbacks at 10.06% with white-sounding names and 6.70% with African-American-sounding names.

    Does not

    Current application volumes or channels; the study predates online application systems.

    Evidence details
    Supports (full)
    Identical résumés received callbacks at 10.06% when carrying a white-sounding name and 6.70% when carrying an African-American-sounding name. The same résumés produced an 11.88% callback rate in Boston and 8.61% in Chicago.
    Does not support (full)
    Newspaper advertisements in two cities, in four clerical and sales occupation families, more than two decades ago and before online application systems existed. It does not describe current application volumes, channels, or rates.
    Measure
    Rate
    Counts
    Résumés receiving a callback divided by Fictitious résumés sent in response to advertisements
    Unit
    Researcher-submitted résumés
    Population
    Fictitious résumés answering newspaper help-wanted advertisements in sales, administrative support, clerical and customer service categories
    Geography
    Boston and Chicago, United States
    Period
    Boston, July 2001 to January 2002; Chicago, July 2001 to May 2002
    Sample
    4,890 observations
    Method
    Randomised correspondence experiment. Roughly four résumés were sent per advertisement — two of higher and two of lower quality — with names randomly assigned. A callback was recorded as employer contact for an interview.
    Found at
    Table 1, rows 'All sent resumes', 'Boston' and 'Chicago'
    Version read
    NBER Working Paper 9873, July 2003
    Notes
    Published in the American Economic Review in 2004. The working paper's Table 1 gives 10.06% and 6.70%; its own narrative text says 10.08%, and the published version is commonly cited with slightly different figures again. The Table 1 values from the version read are used here.
    Record
    Published Jul 1, 2003 · Accessed Aug 21, 2026

  4. arXiv

    Academic · Tier 1 · Sample

    36,880 applications · new US college graduates · 2016–2017

    Sharon Braun, Jonathan Bushnell, Zachary Cowell, David Dowling, Samuel Goldstein, Andrew Johnson, George Miller, John M. Nunley, R. Alan Seals, Mingzhou Wang

    Hiring Discrimination and the Task Content of Jobs: Evidence from a Large-Scale Résumé Audit

    Supports

    The overall callback rate was approximately 15%, ranging from 5% in business and financial operations to 25% in sales.

    Does not

    An interview rate: callback includes requests for further information. Not peer reviewed.

    Evidence details
    Supports (full)
    Across 36,880 applications to 9,220 advertisements for new college graduates, the overall callback rate was approximately 15%, varying from 5% in business and financial operations to 25% in sales, with office and administrative support at 10% and management at 12%.
    Does not support (full)
    Callback explicitly includes requests for further information, so this is not an interview rate. It covers new college graduates only, rests on data collected in 2016 and 2017, and has not completed peer review.
    Measure
    Rate
    Counts
    Applications receiving a response expressing interest, including interview invitations and requests for further information divided by Fictitious applications submitted by the researchers
    Unit
    Researcher-submitted applications
    Population
    Fictitious applications submitted on behalf of new United States college graduates
    Geography
    United States, all 50 states, with 39% of postings in California, Texas, Florida, Illinois and New York
    Period
    April to July in each of 2016 and 2017
    Sample
    36,880 observations
    Method
    Large-scale résumé audit. Callback is defined by the authors as an employer response expressing interest in the applicant, including an interview invitation or a request for additional information.
    Found at
    Section 3.4, 'Employer Responses'; design in Section 3.1
    Quoted
    “The overall callback rate is approximately 15 percent.”
    Version read
    arXiv v2, 23 July 2026 — preprint, not peer reviewed
    Record
    Published Apr 2, 2026 · Accessed Aug 21, 2026

  5. National Bureau of Economic Research

    Academic · Tier 1 · Sample

    3,294 employed and 228 unemployed respondents · US · 2013–2017

    R. Jason Faberman, Andreas I. Mueller, Ayşegül Şahin, Giorgio Topa

    Job Search Behavior among the Employed and Non-Employed

    Supports

    Employed job seekers received the most employer contacts and interviews despite searching about half as intensively as the unemployed.

    Does not

    Any conversion rate: the survey publishes counts, and its application and interview measures span different years.

    Evidence details
    Supports (full)
    In a nationally representative United States survey, people who were employed and looking for work received the greatest number of employer contacts and interviews, and nearly the most offers, despite searching about half as intensively as the unemployed.
    Does not support (full)
    The survey publishes counts, not a conversion rate, and none should be derived from it: the application and interview measures are drawn from different survey years, and a ratio of two averages is not the typical person's rate. It describes 2013 to 2017 and not the current market.
    Measure
    Count
    Counts
    Applications, employer contacts, interviews and offers recalled
    Unit
    Surveyed individuals
    Window
    The four weeks before the survey
    Population
    United States individuals aged 18 to 64, excluding the self-employed. Table 5 reports 3,294 employed and 228 unemployed respondents at the time of survey.
    Geography
    United States, nationally representative
    Period
    October 2013 to 2017 waves; the job-interview measure is available only from 2014 onward
    Sample
    3,294 observations
    Method
    Annual October supplement to the Federal Reserve Bank of New York's Survey of Consumer Expectations. Applications, contacts, interviews and offers are self-reported against a four-week recall window.
    Found at
    Table 5, 'Search Outcomes by Labor Force Status'
    Quoted
    “those who are employed and looking for work receive the greatest number of employer contacts and interviews, and nearly the most offers, despite the fact that their search effort is about half that of the unemployed”
    Version read
    NBER Working Paper 23731, August 2017
    Record
    Published Aug 1, 2017 · Accessed Aug 21, 2026

  6. ERE Media

    Secondary analysis · Tier 3 · Documentation

    Illustrative recruiting funnel · no sample disclosed · 2013

    John Sullivan

    Why You Can't Get A Job … Recruiting Explained By the Numbers

    Supports

    Describes a funnel in which 4 to 6 of every 100 completed applications are invited for an interview.

    Does not

    Any benchmark: no sample size, time period, geography, or method is disclosed.

    Evidence details
    Supports (full)
    Describes a recruiting funnel in which, of 1,000 people who see a job post, 100 complete an application and 4 to 6 are invited for an interview — a rate of 4% to 6% of completed applications.
    Does not support (full)
    The funnel is attributed to a consultancy with no sample size, time period, geography, or method disclosed, so it cannot establish a benchmark for anyone. It is cited here only because it is a traceable origin of the '4 to 6 interviews' figure and states its own denominator.
    Measure
    Rate
    Counts
    Applicants invited for an interview divided by 100 completed applications
    Unit
    Completed applications
    Found at
    Article body, recruiting-funnel walkthrough
    Quoted
    “100 will complete the application, 75 of those 100 resumes will be screened out by either the ATS or a recruiter, 25 resumes will be seen by the hiring manager, 4 to 6 will be invited for an interview”
    Notes
    Tier 3. Included because tracing where a repeated figure comes from is part of this page's argument, not because it supports a rate. No primary evidence for its funnel was located.
    Record
    Published May 20, 2013 · Accessed Aug 21, 2026

  7. Jobvite

    Secondary analysis · Tier 3 · Documentation

    Published benchmark figures · no methodology disclosed · 2023

    7 Benchmark Metrics to Improve Your Recruiting Funnel

    Supports

    Publishes an 8.4% application-to-interview figure alongside other funnel benchmarks.

    Does not

    The figure itself: its population and denominator are never stated.

    Evidence details
    Supports (full)
    Publishes an 8.4% application-to-interview figure alongside other funnel benchmarks, without disclosing a sample size, time period, number of companies, or methodology.
    Does not support (full)
    The figure cannot be evaluated or compared, because the population and denominator behind it are not stated. It is recorded here as an example of a circulating unsourced benchmark, not as evidence of a rate.
    Found at
    Article body, benchmark list
    Notes
    Tier 3. Cited to document the absence of methodology behind a widely repeated figure. No primary source for it was located.
    Record
    Published Feb 5, 2023 · Accessed Aug 21, 2026

Claims this answer bears on

Widely repeated statistics about the same question, each traced to whatever source it has.

Related