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Learn · Before the conversation: online presence

Formulas for attraction, checked

Lesson 6 of 6 in this module · about 6 minutes · Romantic communication

The idea

A percentage split for attraction is a claim like any other: ask who was measured, how, and whether it says anything about one person.

The Personal Brand Creator Pro website states such a split. It is a marketing claim with no source, and this course does not rely on it 1.

The site gives a different split for men, and it cites no source for either 1.

Here are seven questions to ask of any claim like this.

  • Population. Who was measured?
  • Measure. Swipes, ratings, stated importance, or real choices?
  • Relationship type. A swipe, a first date, or a partner of years?
  • Self-report or behavior. What people say they value, or what they do?
  • Additivity. Do qualities really add up as separate slices of 100?
  • Review. Was it published, checked or replicated, or is there no source?
  • Variation. Does it differ between people? A group average describes no one in particular.

Now apply them to the site’s split. It names no source, so it gives no population, no measure and no review.

Other evidence points away from a formula. No study found supports a fixed-percentage split of attraction. The closest studies report effect sizes for specific attributes in specific settings 2. In one study of swiping decisions, physical attractiveness carried the most weight among the attributes tested 2. That is swipes on profiles in one setting. It is not a formula, it is not who people come to like after meeting, and it is not advice 2.

What people say also differs from what they do. Studies from one research group found that self-reported traits and stated preferences, measured before people met, predicted poorly whom a given person came to desire 3. Those were speed-dating and early-interest samples, so the finding does not cover partners of years 3.

So a formula like this tells you nothing about any one person, and nothing about what to change in yourself.

Why it matters

In this course, a confident number is a claim to check, not knowledge. It can steer effort toward staged symbols or worry about the wrong thing, and it says nothing about the person in front of you.

What it looks like

Example

Your friend Diego, 30, sends you the split that the Personal Brand Creator Pro website states for attraction 1. He asks whether to change his photos to show more “lifestyle”. You go through the questions with him. Who was measured? No source. What was measured? Not stated. Diego decides to show what he actually does. “Evidence of a life is more useful than symbols of a life” is a course principle, not a research finding.

The same moment, handled the other way

Counterexample

You take the split at face value and plan your photos around “status” symbols that are not yours. A number with no source becomes a plan, and the plan counterfeits a life.

It fails because a number with no source gave you nothing to check. Curate reality; don’t counterfeit it.

The one thing to remember

The rule

When a claim puts a percentage on attraction, ask who was measured, and how, before believing it.

Go through the seven questions. If the claim has no answer to one, treat it as unproven. Then look at the person in front of you, not at an average.

Try it now

Item 1 of this lesson’s set: No source. Look for which question the claim fails first.

Open the exercise set

In the real world (optional)

If you like, the next time you see a number attached to attraction, ask yourself once who was measured and how.

What this doesn’t tell you

The absence of a supporting study is a search result, not proof that nothing exists 2. The studies cited are specific settings, and nothing here tells you what any person values.

Why do we believe this?

Each numbered mark in the text opens the evidence behind that sentence. Every claim in this course carries one of five labels.

Well supported
A meta-analysis or several independent studies agree, with no serious failure to replicate.
Supported with caveats
Several studies agree, with stated limits such as student samples or lab settings.
Plausible
One study, or indirect evidence from a nearby question.
Speculative
An idea or argument that hasn't been directly tested.
Marketing claim
Something a seller says about their product, without published evidence.
  1. Marketing claimThe Personal Brand Creator Pro site states that women are attracted to men by lifestyle (65%), status (25%) and looks (10%), and men to women by looks (65%), personality (25%) and status (10%), with no source for the split.

    Why this label

    Something a seller says about their product, without published evidence. A numerical attraction formula presented as fact. It is the kind of claim the claim-audit exercise questions: population, measure, relationship type, additivity.

    What it does not show

    • No source cited
    • Percentages are presented as universal

    Who it applies to

    A pattern across groups of people, not a statement about any one person.

    Sources

  2. PlausibleNo study found supports a formula in which attraction splits into fixed percentages for lifestyle, status and looks; the closest studies report effect sizes for specific attributes in specific settings, and in the one real-world swiping study attractiveness dominated.

    Why this label

    One study, or indirect evidence from a nearby question. Demonstrates: the closest evidence is Witmer et al. (2025; 5,340 swipes, 445 daters), where a conjoint analysis found physical attractiveness dominating the selection decisions, with intelligence, height, job, bio and homophily effects 7 to 20 times smaller; and Joel et al. (2017) and Eastwick et al. (2022), which partition variance or test predictors and do not give additive weights. Existing claims: attraction-foundations-c2 (attractiveness is among the strongest predictors in first meetings) and evolutionary-mating-preferences-c2. Lifestyle is not measured as a category in these studies. Marketing framing: precise percentages for what attracts people, such as lifestyle, status and looks (common framing, unattributed). Bears on A4: §49 claim audit; supports the audit questions (population, measure, relationship type, self-report or behaviour, additivity, individual variation) and blocks showing such percentages as scientific fact.

    What it does not show

    • A 'No study found' claim: searched Crossref and OpenAlex for stated-versus-revealed preference, policy-capturing, conjoint and relative-importance studies of looks, status and personality; other studies exist (for example Li et al. 2002, Hitsch et al. 2010, Sprecher 1989) but were not read in full or lacked an abstract here.
    • Witmer et al. (2025) covers one app's swiping decisions, is a field study of selection not a statement of what people 'care about', and needs hand-checking of its effect-size wording.
    • The attractiveness dominance finding is a population pattern in one setting; it does not give a percentage of importance and is not additive across contexts.
    • No replication of the conjoint study found; self-reported and stated weights measured elsewhere often diverge from choices (see attraction-foundations-c3).

    Who it applies to

    A pattern across groups of people, not a statement about any one person.

    Sources

  3. Supported with caveatsStudies of speed dating and early romantic interest found that self-reported traits and stated preferences, measured before people met, did a poor job of predicting who a given person would desire; perceptions of the partner mattered more.

    Why this label

    Several studies agree, with stated limits such as student samples or lab settings. Demonstrates: Joel, Eastwick and Finkel (2017; two speed-dating studies) found that models using 100+ pre-event measures could predict some general tendencies to desire or be desired, but could not predict desire for specific partners; Eastwick et al. (2022; 208 singles, 7 months) found robust effects of perceptions of the partner and no ideal-preference-matching effect. See attraction-foundations-c3 and evolutionary-mating-preferences-c3 for the stated-versus-revealed meta-analytic picture. Marketing framing: attraction is predictable from a person's type or checklist (common framing, unattributed). Bears on A4: §49 claim audit; supports 'what people say they want is not what they respond to', and limits any lesson that predicts attraction from a profile.

    What it does not show

    • Both studies come from the same research group; the abstracts describe speed-dating and single-adult samples but not their country or age range; replication by other groups was not checked.
    • Machine-learning prediction of self-reported interest; null for relationship variance in these designs does not prove compatibility is unpredictable in principle.
    • Self-report outcomes, not later relationship success.
    • Variance-explained figures are not weights for how much a trait 'matters' and must not be shown as such.

    Who it applies to

    A pattern across groups of people, not a statement about any one person.

    Sources

More practice

The rest of the set, Evidence check: percentage formulas for attraction: 4 items, about 4 minutes. Decide what the evidence supports, and how well. Open the exercise set.