[3rd3]

Even though p-values can be hacked, they are very useful when they aren't. At p = 0.1 I'd ignore the finding because there would be a 10% chance it was explained by random chance. p = 0.01 would pique my interest. p < 0.001 I'd accept it as true, but I'd still watch out for systematic biases such as comparing new to old factories.

[nerdponx]

Right, if you're at the point of constructing some kind of principled estimate of variation in the data then I think you have a pass to at least talk about "significance". But in that case I'm sure you're aware that this requires a particular hypothesis test in mind, not just an abstract notion of "significance", and that p-values interpreted as "strength of evidence" are problematic.