Methodology
Every calculator on this site begins with a question people rarely say out loud. How often do most people actually have sex? What does an average net worth look like at 35? And how long does a typical dry spell run?
The answers are sitting in academic research, mostly unread by anyone outside it. Finding them, rebuilding the distribution behind the headline number and showing you where on it you land is the whole job here. Below are the sources, what gets recorded when you use a calculator, and what happens to it afterwards.
Find The Norm is a percentile calculator platform. It uses peer-reviewed research from sources including the CDC, ONS, WHO, Federal Reserve, General Social Survey, and Kinsey Institute to generate population distributions. Users enter a personal data point and Find The Norm returns their exact percentile rank within that distribution.
Where the numbers come from
The academic baseline
Every percentile on this site rests on published research: government surveys, clinical datasets, academic studies and published polls from bodies like the General Social Survey, the ONS, the Kinsey Institute, the CDC and the Federal Reserve.
Each distribution is meant to trace to a named primary source rather than to a secondary one quoting it, and when a check finds one that doesn't, the page gets corrected. The study, its authors, the journal, the sample size and the year of publication all sit in the "About this data" section on the calculator page, and each should lead back to the original.
Where research disagrees, or where population samples differ materially from one country to the next, the page says so and uses the most broadly applicable dataset it can.
Our own submission data
Finish a calculator and your answer is recorded anonymously, alongside your age group and, where the calculation needs it, your gender. That record does not replace the academic baseline. It sits next to it.
It gets used to watch how this site's users compare with the published research and, once a sample is big enough, to produce original findings. Any time one of those is published, the type of data behind it is stated outright.
"Among Find The Norm users, X" = our own submissions.
What we collect and what we don't
We Collect
Use a calculator and your inputs are recorded anonymously. The record holds the measurement or behaviour you typed in, plus whatever demographic detail the calculation needs in order to mean anything.
- The measurement or behaviour entered
- Age group
- Gender (where relevant)
We Do Not Collect
Submission data exists to produce aggregated population findings. Nothing in it builds a user profile, points advertising at you, or reaches a third party in any identifiable form.
- Name
- Email address
- IP address
- Device identifiers
- Any personally identifiable information
How we decide what to publish
Our own submission data has to clear strict thresholds before any finding based on it gets published. Where a sample falls short, the page says so instead of quietly dropping the caveat.
What makes this data useful
Honest responses
People tell us things they will not tell a researcher. Nobody is sitting across a table when you use one of these calculators, and you are asking for your own sake rather than answering for somebody else's study. Survey methodology research finds again and again that self-directed anonymous reporting produces franker answers on sensitive topics than an interviewer-administered survey does.
Current data
Many of the academic baselines under these calculators come from studies run 5 to 15 years ago. Submission data is live. Where the two pull apart, the gap between them is usually the thing worth writing about.
Broad coverage
There is no academic dataset that tracks sexual frequency alongside net worth alongside intrusive thought frequency alongside dry spell duration. Nobody funds a study that wide, so the ground between those questions goes uncovered. Ours covers it.
Where this data has limits
We are frank about this, because the limits are real.
Selection bias
People who go looking for a personal statistics calculator are not a random sample of anything. They are curious about where they stand, which usually means they already suspect they might be unusual. So this data almost certainly overrepresents the tails of a distribution relative to the population median.
Self-reporting bias
Anonymity helps with candour and does nothing at all for memory. Even so, self-reported answers carry distortion and inconsistent self-assessment, and on any calculator that asks for an estimate rather than precise recall, systematic error is likely.
Demographic skew
The user base here is mainly English-speaking and skews toward adults aged 18 to 45. Nothing confirms that it represents any national or global population. Read every finding from submission data as a finding about Find The Norm users and nobody else.
Who checks the numbers
One person does. James Maclean builds every calculator on this site, reads the source table himself, re-derives the percentile maths from the published figures, and reads the page through before it goes live. There's no content team here and no automated pipeline turning out pages nobody has looked at. That's slower, and it's why the site holds a few hundred calculators rather than a few thousand, but it also means a named person answers for where every number on a page came from.
Each page carries the date it last changed, taken from the site's own version history rather than stamped on at build time. So if a page says July, something on it moved in July. When a source publishes a new wave of data, the calculator gets rebuilt against the new table and the date moves with it.
Spot a figure that doesn't match its source, or a study that has since been superseded, and the address is [email protected]. James reads those himself, checks the claim against the source, and either fixes the page or writes back explaining why the figure stands.
When our data and the research disagree
When submission data pulls away from the published academic baseline, the gap gets investigated before anything is concluded or published. Selection bias, a recency effect, an anonymity effect and demographic mismatch are the four usual explanations, and one of them is nearly always the answer.
Divergences get reported with the most plausible explanation stated plainly, and no causal claim is stretched past what the data will carry.
Corrections
When a data error turns up, whether in a source or in how we applied it, it gets corrected promptly, and the date on the affected page moves with the change.
And if a press release or a published finding carries an error, we go to the outlet directly and issue a correction notice.
[email protected]For press and researchers
Every press release and data pitch from Find The Norm carries the sample size, the collection period, a plain distinction between academic baseline and submission finding, and contact details for an independent expert who is willing to comment on it.
"Data collected from [N] self-reported submissions to Find The Norm between [date range]. Submissions are anonymous and voluntary. Results reflect the Find The Norm user population and may not be representative of the general population. As with all self-reported data, results are subject to selection bias. Full methodology at findthenorm.com/methodology."