Comment by thyristan

Comment by thyristan a day ago

3 replies

There is a known maximum error introduced by those simplifications. Put the other way around, Einstein is a refinement of Newton. Special relativity converges towards Newtonian motion for low speeds.

You didn't really miss anything. The article is incomplete, and wrongly suggests that something like "false" even exists in statistics. But really something is only false "with a x% probability of it actually being true nonetheless". Meaning that you have to "statistic harder" if you want to get x down. Usually the best way to do that is to increase the number of tries/samples N. What the article gets completely wrong is that for sufficiently large N, you don't have to care anymore, and might as well use false/true as absolutes, because you pass the threshold of "will happen once within the lifetime of a bazillion universes" or something.

Problem is, of course, that lots and lots of statistics are done with a low N. Social sciences, medicine, and economy are necessarily always in the very-low-N range, and therefore always have problematic statistics. And try to "statistic harder" without being able to increase N, thereby just massaging their numbers enough to get a desired conclusion proved. Or just increase N a little, claiming to have escaped the low-N-problem.

syntacticsalt a day ago

A frequentist interpretation of inference assumes parameters have fixed, but unknown values. In this paradigm, it is sensible to speak of the statement "this parameter's value is zero" as either true or false.

I do not think it is accurate to portray the author as someone who does not understand asymptotic statistics.

  • thyristan a day ago

    > it is sensible to speak of the statement "this parameter's value is zero" as either true or false.

    Nope. The correct way is rather something like "the measurements/polls/statistics x ± ε are consistent with this parameter's true value to be zero", where x is your measured value and ε is some measurement error, accuracy or statistical deviation. x will never really be zero, but zero can be within an interval [x - ε; x + ε].

    • syntacticsalt a day ago

      As you yourself point out, a consistent estimator of a parameter converges to that parameter's value in the infinite sample limit. That limit is zero or it's not.