One analogy: non-mathematical, not formalized, not written in English, and attempts to translate generally fail.

See [*] for a critique of null hypothesis and related techniques from a Bayesian perspective. To cite:

My work in power analysis led be to realize that the nil hypothesis is always false. [...] If it is false, even to a tiny degree, it must be the case that a large enough sample will produce a significant result and lead to its rejection. So if the null hypothesis is always false, what's the big deal about rejecting it?

[*] J. Cohen (1994). `The Earth Is Round (p < .05)'. American Psychologist 49(12):997-1003. [pdf].