This article attacks falfsificationism in part on the basis that scientists don’t seem to actually behave according to “naive falsficationism” in which you supp

This article attacks falfsificationism in part on the basis that scientists don’t seem to actually behave according to “naive falsficationism” in which you supposedly should immediately throw out a theory as soon as you get some conflicting empirical data. The article notes that collecting empirical data to falsify a theory depends on auxiliary theories (eg testing the idea that smoke causes cancer in rats depends on our ability to precisely define and identify when cancer is present) and so most scientists will question these auxiliaries rather than the main theory if conflicting data comes out. This is presented as evidence that scientists don’t actually practice falsficationism. This whole discussion completely ignores uncertainty and the fact that theories describe not only relationships between variables but also involve parameters that need to be estimated. If I fill a rat cage with smoke and the rats don’t get cancer, I don’t immediately assume all previous studies were wrong. This isn’t ignoring my duty to falsify, it’s realizing that the theory that smoking causes cancer describes a causal relationship between variables (smoking -> cancer) it also implicitly or explicitly depends on parameters (in this case, how much and how long a rat must be exposed to smoke to cause cancer at some incidence rate). Given that many other studies found the causal relationship, it is more likely that my experiment messed up the parameters of the causal model (eg I didn’t administer smoke long enough) rather than messed up the causal relationship. And parameter estimation is subject to uncertainty. Now if I’m absolutely sure I’m getting all my model parameters correct, and I can only be confident if other people get the same results. Then I’m moving toward falsifying the theory. The point is that science involves positing theories, and theories are descriptions of causal relationships and generally involve parameters. Much of science is just estimating model parameters given that we assume some model to be tentatively true. You assume your model to be tentatively true, eg smoking causes cancer, now you need to estimate the effect size which is a parameter problem.