I think you have presented a fallacy: > If an experiment is run that says the sky is blue, then its invalidation does not mean that the sky is not blue. If an

I think you have presented a fallacy: > If an experiment is run that says the sky is blue, then its invalidation does not mean that the sky is not blue. If an experiment's hypothesis is that the sky is blue and the sky is indeed blue, then there is nothing wrong and there is nothing to be invalidated. If an experiment's hypothesis is that the sky is blue because there is a bunch of leprechauns throwing blue Skittles at it, and there are evidence that this not the case; then the experiment is to be invalidated and, in your own words, "[i]t means we no longer have evidence for that conclusion." If you don't have the evidence to support your hypothesis, then you can't draw any conclusions except for, and at best, that there is no evidence to support your hypothesis. To put it into the context of the running example, this translates to the sky is blue but it's not because leprechauns are throwing blue Skittles at it. You can't say just because my hypothesis about leprechauns is invalidated, it doesn't mean that there are not leprechauns throwing skittles at it—because that's simply just what you believe and wish to be the case; and a good scientist should be impartial to beliefs. More importantly, the whole point of the article is beyond invalidating the SPE. It's, in my opinion, a great piece about much deeper problems in scientific or, well, "scientific" methods.