A few authors over the years have suggested that, in most cases, effects (and all effects are causal by definition) cannot be exactly zero, at least in fields like health and the social sciences. For example:
“All we know about the world teaches us that the effects of A and B are always different — in some decimal place — for any A and B. Thus asking “Are the effects different?” is foolish.”
(Tukey (1991); doi:10.1214/ss/1177011945)
“In any hypothesis testing situation, a reason can be found why a small difference might exist or a small effect may have taken place.”
(Nester (1996); doi:10.2307/2986064)
“Who cares about a point null that is never true?”
(Little RJ. Comment. The American Statistician. 2016;70(suppl))
“Also, remember that most effects cannot be zero (at least in social science and public health)”
(Gelman, Carlin (2017); doi:10.1080/01621459.2017.1311263)
“As is often the case, the null hypothesis that these physical changes should make absolutely zero difference to any downstream clinical outcomes seems farfetched. Thus, the sensible question to ask is “How large are the clinical differences observed and are they worth it?” — not “How surprising is the observed mean difference under a [spurious] null hypothesis?””
(Gelman, Carlin, Nallamothu (2018); http://www.stat.columbia.edu/~gelman/research/unpublished/Stents_submitted.pdf)
While this has obvious relevance for the usual conception of the null hypothesis, and this was the context for each of the above quotes, it may have some additional influence on the inferences researchers make - perhaps framing the question in terms of whether the difference is clinically meaningful, rather than asking if an effect or difference exists, would change how results are sometimes viewed and interpreted?
Clearly, there would be hypothesised effects that really are exactly zero, meaning 0.00000…, or are close enough that they cannot be distinguished from zero. But these cases would not seem to be common outside of the physical sciences.
I’ve posed this topic to see if anyone:
a. disagrees
b. thinks that it doesn’t matter
c. has a different interpretation of the above statements, or
d. has a thought on how such a viewpoint might affect inferences
(or e. anything else)
thanks,
Tim