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The last reason to reject a hypothesis is because it defies narrow conceptions of common sense.
The decision to accept or reject a hypothesis is associated with the so-called significance and power of the test.
When we accept or reject a hypothesis, there is always a chance that our decision is mistaken.
In such contexts, it is often necessary to accept or to reject a hypothesis (e.g., the efficacy of a drug) in order to make effective decisions.
In statistical hypothesis testing, we will reject a hypothesis if an outcome is observed that is improbable (supposing the hypothesis were true) at some set significance level; in that case, the outcome is significant.
Not wanting to be one to reject a hypothesis without testing it, I decided to live with the possibility for a few days that all of it -- the recurrent infections, the fevers, the dizziness, the bizarre neurological symptoms -- really was, as my neurologist believed, all in my head.
Similar(45)
Inverse (r-mode) analysis using Type 2 SIMPROF decisively rejects a hypothesis that species are not associated with each other.
In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information.
One should notice that not rejecting a hypothesis does not mean the hypothesis is accepted.
This can produce situations where rejecting a null hypothesis does not provide new information, since the only thing we can deduce by rejecting a hypothesis is that there is an effect, but not in what direction or magnitude (Falk & Greenbaum, 1995, Lecoutre, 1996).
Such conventional standards are especially prolific in theoretical research where it does not make sense to specify application-oriented utilities of accepting or rejecting a hypothesis (cf. Wilholt 2013).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com