Fabelwaren
Honest psychometrics

The test that sells you nothing before you know your result.

Science-based tests. Your result is free. Always.

No account. No e-mail. No subscription trap.

231,953 people in the Big Five comparison sample
0.88 shadow test reliability (Cronbach's alpha)
8 languages, each translation verified
100 % of results free — always
Three tests

Choose your window

The honest thinking test

In calibration

An honest uncertainty range instead of a made-up score.

4 rounds of 11 puzzles · about 5 minutes per round

You can stop after any round.

Result free · detailed report 4.99 €, one-time

Start the thinking test

See what a result looks like →

The shadow test

Four sides we rarely talk about.

24 statements · about 4 minutes

No verdict and no diagnosis.

Everything free — the pair comparison too

Start the shadow test
What to hold us to

Result always free

In every test you see your result immediately and in full. Only the optional detailed report costs a one-time 2.99 € or 4.99 €. No subscription.

Honest about limits

We show the measurement error instead of hiding it, and we name no band when the range is too wide for one. No inflated claims.

Your data stays yours

No account, no e-mail required, no data sharing. Your result lives behind an anonymous link only you know, and you can delete it anytime.

About Fabelwaren

Who is behind this

Fabelwaren is a small independent project from Austria, with no investors, no ad network, no data trade. It is funded solely by the optional detailed report: one price, once, no subscription.

So you get your result before paying anything, and we say openly what an online test can NOT do. A business living on ads has to keep you as long as possible. A business living on a single purchase has to convince you once, and that only works honestly.

Figures & sources

  • Questions from the IPIP-NEO-60 and IPIP-HEXACO — freely available scientific questionnaires
  • Comparison values from published norm samples, documented with sources
  • Reliabilities checked against a public dataset — including the price of our deliberate cuts

Method & limits →