Stephen T’s Blog Spot

Last time I argued that the heart of a credible causal claim is hunting for the explanations you would rather were not true. That is harder than it sounds, and the reason is the most human bias in all of research: we instinctively look for evidence that we are right, not evidence that we are wrong. It rarely feels like a bias. It feels like confidence.

The cleanest demonstration is sixty years old. In 1960, the psychologist Peter Wason showed people the sequence 2, 4, 6, told them it followed a hidden rule, and asked them to discover the rule by proposing their own triples, learning only whether each one fit. Most quickly formed a hypothesis, often something like even numbers rising by two, and tested it with triples like 4, 6, 8 and 10, 12, 14. Each came back yes. Confidence climbed, they announced their rule, and they were wrong. The actual rule was simply any increasing numbers. They had never tried a triple that could fail, like 1, 2, 3 or 6, 5, 4, so they never learned their rule was too narrow. The confirming examples felt like progress. They were a trap.

That is the whole problem in miniature. Piling up evidence consistent with your idea tells you very little, because the evidence that would actually test the idea is the evidence that could contradict it. Raymond Nickerson, in a landmark 1998 review, showed how far this reaches: confirmation bias shapes what evidence we go looking for, how we read ambiguous evidence, even what we later remember. In research it decides which studies we cite, which analyses we run and quietly keep, which interview quotes jump out as themes, and which inconvenient cases we explain away. None of it feels like cheating. It feels like the data agreeing with us.

The antidote is old, and it connects to the falsifiable theory of change I wrote about earlier. Karl Popper’s point was that you learn the most from trying to refute an idea, not to support it. So the question to put to your own work is not what supports my conclusion, but what would show it is wrong, and have I genuinely gone looking for that? A finding you only tried to confirm has barely been tested. A finding that survived a serious attempt to break it has earned some trust.

Here is the uncomfortable twist. Willpower is a weak defense. In follow-up studies, simply telling people to consider disconfirmation did not reliably make them better at it; we cannot easily out-stubborn our own minds in the moment. That is why the durable fixes are structural, not attitudinal. Pre-register the hypothesis and analysis before you see the data, so you cannot later redefine success to fit the result, the same guard against p-hacking I have written about. Assign someone whose job is to argue the opposite, a red team or adversarial reviewer. Analyze blinded where you can, so you do not know which number is the one you hoped for. The aim is to build disconfirmation into the process, so being right does not depend on you being heroically objective on a Tuesday afternoon.

This only gets sharper as we lean on AI assistants, which will cheerfully elaborate whatever hypothesis we hand them. A tool that agrees with you is comfortable and dangerous. Ask it for the strongest case against your finding, not just for support.

So here is my question: Before you analyze, do you name what would change your mind, and who in your process is actually responsible for trying to prove the conclusion wrong?