[jjk166]

Falsifying the logical inverse is fine. The key though is that showing the inverse of what you believe can not be true is much harder than showing that what you believe might be true.

Preregistration only helps with a select set of researcher bias, specifically where they retroactively change parts of their model to get the intended result. However it does nothing to protect against bias baked into the model ahead of time. Presumably the same reasons that researchers are strongly politically opinionated are the reasons they emphasize certain things over others in their models long before they see any data.

[chickenimprint]

Falsifying the logical inverse of X is identical to verifying X. There's nothing about negation that does anything here. You're making the same mistake people make when claiming "You can't prove a negative".

> The key though is that showing the inverse of X can not be true is much harder than showing that what X might be true.

This is nonsense modal logic. You're saying ¬◻¬X, which if necessity and possibility are duals, is equivalent to ◇X, and otherwise an irrelevant statement. The inverse of X is ¬X. ¬¬X is logically equivalent to X.

[jjk166]

> Falsifying the logical inverse of X is identical to verifying X.

That's exactly the point. The issue with positivist hypotheses is that you can find evidence that supports, but does not actually verify, the claim. This seems convincing until you try to flip it around. So for example if a prediction of my model is that the sun will rise tomorrow, and the sun does rise tomorrow, that seems like it supports my model. But if my model can be wrong and the sun would still rise tomorrow, then looking for the sun rise was never going to answer the question.

If you are doing a test that will actually verify X then bias doesn't matter.

[chickenimprint]

I'm not sure what this has to do with positivism.

Data is evidence for all models that predict the data. It doesn't matter that almost all of those models are extremely wrong, while the most accurate models are gross approximations of the truth. Scientists can continue to collect data and narrow the scope of statistically likely models. I don't see how your comment supports your assertion that "the goal of experiments needs to be falsifying hypotheses".

If by verify, you mean prove, I'd say it's not possible to empirically verify anything.