Methodology

Every number on this site, and where it comes from.

This site makes quantitative claims and runs two instruments that collect responses. Both of those create obligations, and this page is where they are met.

Two source tiers

Tier one is peer-reviewed research or nationally representative survey work with published methodology. Tier two is industry or platform data — real numbers from real users, but no peer review and usually no published sampling method.

Only one figure on this site is tier two: the dating app match rates of roughly 5% for men and 44% for women. It is used because no tier one equivalent exists and the direction is not seriously in doubt. It is labelled as industry data wherever it appears.

Derived figures

Overlap percentages and coin-flip numbers are calculated from published effect sizes assuming normal distributions of equal variance. Real distributions are not perfectly normal. Treat these as close approximations, not measurements. The calculation is standard and reproducible.

The one modelled claim

The claim that the perceived gap exceeds the difference in height is a model, not a finding. It assumes message-sending propensity rises with sociosexuality and applies a shift of one standard deviation. That shift was chosen, not estimated. At 0.75 the perceived overlap is about 46%; at 1.0 it is about 38%. Height is 42%.

Anyone who wants the argument to fail should attack the assumption. That is the right place to attack it.

Principal sources

Fourteen sources carry most of the weight. Full citations for everything else appear in the book’s Sources and Notes.

  1. Pew Research Center (2023). From Looking for Love to Swiping the Field: Online Dating in the U.S. Nationally representative survey of 6,034 US adults, fielded 5–17 July 2022.Every usage, motive, unwanted-behaviour and satisfaction figure on this site.
  2. Zell, E., Krizan, Z. & Teeter, S. R. (2015). Evaluating gender similarities and differences using metasynthesis. American Psychologist, 70(1), 10–20.106 meta-analyses, 386 effects. The average psychological sex difference, d = 0.21.
  3. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60(6), 581–592.46 meta-analyses. 78% of measured sex differences small or near zero.
  4. Schmitt, D. P. (2005). Sociosexuality from Argentina to Zimbabwe: a 48-nation study of sex, culture and strategies of human mating. Behavioral and Brain Sciences, 28(2), 247–311.Openness to uncommitted sex across 48 cultures.
  5. Lippa, R. A. (2009). Sex differences in sex drive, sociosexuality and height across 53 nations. Archives of Sexual Behavior, 38(5), 631–651.Effect sizes for sex drive (0.62), sociosexuality (0.74) and height (1.63).
  6. Petersen, J. L. & Hyde, J. S. (2010). A meta-analytic review of research on gender differences in sexuality, 1993–2007. Psychological Bulletin, 136(1), 21–38.Masturbation and pornography differences — and the 26 of 30 that were small.
  7. Eastwick, P. W., Luchies, L. B., Finkel, E. J. & Hunt, L. L. (2014). The predictive validity of ideal partner preferences: a review and meta-analysis. Psychological Bulletin, 140(3), 623–665.Stated preferences fail to predict live romantic evaluation.
  8. Lee, A. J., Sidari, M. J., Murphy, S. C., Sherlock, J. M. & Zietsch, B. P. (2020). Sex differences in misperceptions of sexual interest can be explained by sociosexual orientation and men projecting their own interest onto women. Psychological Science, 31(2), 184–192.Speed-dating study, n = 1,226. Overperception as projection rather than adaptation.
  9. Shapiro, A. G., Peters, R. M. & Ahrens, A. H. (2024). (Mis)estimation of the modal number of desired sexual partners. PLOS ONE, 19(12), e0315291.The most common answer is one — and almost nobody predicts it.
  10. Eyal, T., Steffel, M. & Epley, N. (2018). Perspective mistaking: accurately understanding the mind of another requires getting perspective, not taking perspective. Journal of Personality and Social Psychology, 114(4), 547–571.25 experiments, ~2,608 participants. Why imagining the other perspective fails.
  11. Joel, S., Eastwick, P. W. et al. (2020). Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies. Proceedings of the National Academy of Sciences, 117(32), 19061–19071.86 authors, 11,196 couples. What actually predicts a good relationship.
  12. Berggren, M. & Bergh, R. (2025). Simpson’s gender-equality paradox. Proceedings of the National Academy of Sciences, 122(23), e2422247122.The gender-equality paradox as a methodological artefact.
  13. Gowaty, P. A., Kim, Y.-K. & Anderson, W. W. (2012). No evidence of sexual selection in a repetition of Bateman’s classic study of Drosophila melanogaster. Proceedings of the National Academy of Sciences, 109(29), 11740–11745.The failed replication of the standard story’s empirical cornerstone.
  14. Janicke, T., Häderer, I. K., Lajeunesse, M. J. & Anthes, N. (2016). Darwinian sex roles confirmed across the animal kingdom. Science Advances, 2(2), e1500983.The comparative result that cuts the other way, included for balance.

Not yet live

The instruments on this site do not currently store responses. Before any version that does goes live, this section must carry: a plain-language consent screen shown before the first question; a statement that responses are aggregated and may be published in the book and in public reporting; the minimum fields collected; a stated withdrawal route; and an explicit note that respondents are self-selected and not nationally representative.

Pre-registered prediction

Before collecting any data, the prediction is stated here: the mean perceived gap between the sexes will substantially exceed the measured psychological gap, and the errors will run in approximately the same direction and magnitude for both sexes.

If the data does not support that, it will be published here anyway. That is the point of saying it in advance.