Atlas KaaTai tests sweeping claims against open data β across all 400 German districts, open-ended. We confirm and we debunk. Here is how we calculate, where the method reaches its limits, and how you can verify every result yourself.
How our fact-checks come about β step by step Β· β Overview
We take a sweeping claim of the kind you hear every day ("Where there's a lot of X, there's a lot of Y") and test it against data for all 400 German districts.
Our role is that of the referee: we test claims from every political direction, confirming them just as readily as we debunk them. We have no camp β only data.
Importantly, we describe patterns between regions; we do not judge people or parties. "In districts with feature A, feature B tends to be higher" is an observation about regions, not a verdict about individuals.
We use Pearson's correlation coefficient r. It measures how strongly two variables rise or fall together across the 400 districts β and in which direction.
As a rule of thumb: |r| from about 0.2 is a weak, from 0.4 a moderate, from 0.6 a strong relationship. We state the r value openly in every fact-check β you don't have to take our word for it, you can recompute it.
Every fact-check ends with one of three verdicts:
The verdict always refers to the sweeping wording. "Partly" doesn't mean "who cares"; it means the simple story falls short.
For every active indicator, Atlas shows a confidence score (5β95%). It makes transparent how reliable the regional statement is β providers like Microm or Sinus don't publish such figures; we do.
The score combines two quantities, computed live across all areas of the active level (cells, municipalities, districts, constituencies or federal states):
Interpretation: High β₯ 70% Β· Medium 40β69% Β· Low < 40% ("caution"). With two combined indicators (bivariate mode) the minimum of the individual scores applies. Also shown: n, Ο and the P5βP95 spread.
Exception β absolute counts (population, persons, buildings): their large dispersion is real settlement structure β a village cell next to a city cell β not data uncertainty. For these, Ο does not enter the score; it derives from n alone (β₯ 1000 β 90 Β· β₯ 200 β 75 Β· β₯ 50 β 50 Β· below β 25), and the box marks the indicator as an "absolute value".
Important: the score measures the reliability of the aggregate, not the hit rate per person. A score of 85% means the regional differences are statistically stable β not that 85% of people there match the profile (ecological fallacy).
Some indicators carry neutral marketing names in the Atlas so that maps work in demos and campaign contexts without misreading. The data itself is unchanged official statistics β here is the translation:
The exact technical term always appears in the indicator's tooltip ("Fachbegriff: β¦"). The "Fachbegriffe anzeigen" link in the confidence box (or the URL parameter ?labels=fach) switches back to the original names at any time.
We use exclusively open, freely licensed official data (DL-DE 2.0 or ODbL):
Source and data year appear in every fact-check. Nothing secret, nothing bought.
This is the heart of it: you don't have to trust us. Every fact-check links the interactive map in the Atlas β the very dataset we used. A direct link opens exactly the indicator (or the combination of two indicators) the video was about.
We don't hide the counter-correlations either: if a different variable explains the pattern better, you can see that for yourself in the same map.
Credibility is our most important asset. That's why, across a season, we deliberately mix confirmed and debunked claims from different directions β checking only one side isn't neutral.
We phrase things descriptively, not judgmentally, and name uncertainties openly. Spotted an error or a questionable call? Write to beratung@kaatai.de β we correct transparently and visibly.