That's funny, in ours it was more like: If you don't have the replicates (for the statistics), it didn't happen.

What? How would that be justified?

I've seen this attitude before. The idea goes something like: "If the effect you are seeing is so subtle that you have to resort to statistical tests, it probably isn't a phenotype you should be studying".

In my experience, this usually comes from PIs that worked back in the days of "move bar of light across visual field, see neurons fire". Their experiments were ground breaking and had drastic phenotypes.

On the flipside, I've seen experiments where they are showing that one population of neurons is 2% more dynamic than another population, and sometimes you just wonder if that really matters at all (or if you are really sampling the population well enough)

Actually, that quotation is usually attributed to Ernest Rutherford. This is a common discussion among particle physicists, but of course Rutherford did his work before the statistical interpretation of quantum mechanics became clear. For example, even measuring something like the mass of a particle is inherently probabilistic; for short-lived particles it's usually done by repeating an experiment many times, fitting a gaussian distribution to the decay times and backing the mass out from the time-energy uncertainty principle.

See http://www-stat.stanford.edu/~ckirby/brad/papers/2005NEWMode... for a really interesting discussion of the way things are headed from a statistician's perspective.

To elaborate on what other have said: it's not meant to be taken literally. There will of course be cases (e.g. the LHC) where it's imperative to do precise statistics because it's so expensive to make a measurement and only a few measurements can be reasonably made. But the idea of the quote is that often a researcher is going to be wasting his time if the effect he is looking at is so small as to require heavy duty statistics; the fancy statistical machinery can be a time-sink and distraction from the fact that the underlying effect isn't that interesting.

How would you know anything about your data without doing some statistical analysis. I'm not sure if you're saying some statistics are gathered or that any statistics are hard.

The distinction is between trivial and non-trivial statistics. If I test a drug which cures 90 patients out of 100, vs. a placebo that cures none, I don't need to quote a p-value. The drug works.