Problem:

Question: How does the biology community currently feel with regards to publishing descriptive and effect size statistics rather than significance stats?

Almost every journal article I read in the cell biology field almost always reports things like P values and stats tests to report statistical significance, but should effect sizes be more important to a biologist?

Do we even care if something is statistically significant if the effect size is negligible? Rather than crunch for significance, could one get away with showing things like confidence intervals, eta^2, Cohen's d, and r values instead over P values?

P values tell you the odds that if you assume the null hypothesis is true, then the observation you're making are only 5%(assuming of course P<0.05). However, this can lead to the logical fallacy as noted by Aristotle--theory A predicts that changing X will cause Y.

An experimenter thus performs experiments to manipulate X and sees changes in Y, therefore he/she concludes theory A is supported, which is however completely wrong. Theories B, C, D, E....... could all also predict that X changes Y and may even be better at it

Even if you conclude that your findings "support" theory A, it's still weak because you haven't ruled out all of the other possibilities.

So in order to avoid statistical significance relative to null hypothesis that has all sorts of pitfalls, can one just use descriptive and effect size statistics just as effectively, if not more so?

 Please provide size statistics.