In the language of hypothesis testing you have your null and alternative hypotheses.
So for alternative hypothesis that the sun comes up in the morning, the null hypothesis would simply be that the sun does not come up in the morning.
Each of the negative results, reads to me like a separate 'alternative' hypothesis.
So let's say I claim that the sun goes in a circle in the sky in the morning. The null hypothesis is that it doesn't do that. Perform experiment. Null hypothesis wins. Write up paper! This is a negative result.
The point is that for every result where the alternative hypothesis wins, there are a massive, if not infinite, number of results where the null hypothesis will win. Are these publishable?
> It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so.
??? As I had said originally, that's one of the primary situations where a negative result should be published.
But the huge, huge, huge majority of negative results are trivial and uninteresting. Thus the fundamental issue with negative results is that you have to provide rather more compelling justification for why such results should be published.