This happens because hypothesis testing conflates effect size (how big is the difference between A and B) with uncertainty about that effect size (significance/reproducibility). Confidence intervals are more useful IMHO, as they help untangle these two aspects, for example showing that the difference between A and B is small _and_ reproducible. Bayesian analysis is also a major improvement, as it allows examining both the "null" and "alternative" hypotheses on equal terms, as well as reasoning about our prior beliefs / biases. Unfortunately many areas of science are still stuck with statistical methods from the early 1900's.

But the mole as a "counting unit" is different from OP's idea of assigning units to things like network requests. The mole is just a shorthand for a number, like a dozen or a score. We don't have different kind of "moles" for, say, carbon atoms and water molecules. Or coffee.

Storytelling computers will change the course of human history, says the historian and philosopher.

Comments URL: https://news.ycombinator.com/item?id=35750638

Points: 3

# Comments: 5

This made me laugh :) I'm in academia and this post does remind me of certain students...

This seems like a boneheaded move. What credibility will the EU have in criticizing media censorship in Russia when they engage in censorship themselves?

So if we want to keep domestic production, either consumers need to voluntarily choose more expensive alternatives -- which seems unlikely to happen -- or we have to put tariffs or other restrictions on import of goods. The EU does this for a variety of agricultural products to protect farming, for example.

Or is there another way out?

https://en.wikipedia.org/wiki/Bernoulli_trial

Of course, that's assuming that five guys from the vaccine group didn't get infected at the same after-ski party, or any funny business that violates statistical independence ...