Comment by jimothyhalpert7 | Hacker News Reader

Comment by jimothyhalpert7 | Hacker News Reader

jimothyhalpert7

> They had done the chemical reaction that blew up the lab 175 times before without incident; then, suddenly, something went wrong and the lab went boom and real, actual people died.

I really wish the author would expand more on this. Maybe due to my shallow knowledge of statistics, I've recently become baffled by the fact that an arbitrary number is used as a confidence interval, to state that something is true or false. And I'd guess most of today's world depends on these confidence intervals. Why is it that we're OK with stating that something is true, if it's true 95% of the time? Or is it the case of good enough, it it ain't broke, don't fix it (until it isn't)?

We are OK with it because almost always we don't know a better way. Every time we use statistics and have certain finite sample size, these confidence intervals will emerge.

The only place we are ok with this is in the social sciences where rigorous causation can never be proven. No physics theory is considered 'valid' because of a confidence threshold. Pharmacology and epidemiology flirt with this too, because the underlying causal mechanisms can be fiendishly difficult to determine, but we see 'new evidence' stories almost every day that flush whatever last week's 'wisdom' was.

>we see 'new evidence' stories almost every day that flush whatever last week's 'wisdom' was.

I guess the process you are talking about involves creating a new model which is able to account for the causal relationship for a larger set of observations, compared to the previous model. In this way, the previous model can be either flushed, or if the conditions under which it fails are known, we can use it for specific cases. Isn't physics prone to the same effect, only to a lesser frequency? Which might be worrisome, because if the opportunity to revise a model comes every 100 years, that might mean that you'll spend your whole life interpreting the world through an inferior model.

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