[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.