Scientists are trained to look for evidence of their own biases, and to consider the possibility that their hypotheses are wrong. In fact, hypotheses are framed in terms of the "null" - the baseline assumption that the thing you think is true is not.

In every grant, you have to:
- lay out your safeguards against bias, point by point
- discuss what happens if your initial hypothesis is wrong
- guard against linked Aims that rely on assumptions that your hypothesis is right

You basically can't get funded unless you show rigor and an ability to roll with unexpected findings.

So if you're wondering what the difference is between a scientist who has been trained in this method, and someone at home with the internet, a dogmatic view, and an ability to cherry pick data to support that view, this is it.

It's not (necessarily) about content expertise (though that's key too). It's about discipline, method, and rigor.

I wish I could say it's easy. But even seasoned researchers have to work hard at maintaining that objectivity and the practice of looking critically at the biases in their own work that always threaten to sneak in.