# Why use 95% confidence interval?

May I ask why $95\%$ confidence is so commonly used? Does it have anything to do with $\frac{d}{d\alpha}e_n(\alpha)$, where $e_n(\alpha) = Z_{\alpha/2}\frac{S_n}{\sqrt n}$? (My professor asks me to evaluate this derivative at $\alpha = 0.05$, given $S_n = 4.7, n = 100$.)

• It's arbitrary. Mar 17, 2018 at 20:11
• I am 95% sure that the number is arbitrary :-). Maybe a vigesimal hangover? Mar 17, 2018 at 20:13
• There is a debate currently going on in various communities as to whether a lower $P$-value should become the tacit standard.
– amd
Mar 17, 2018 at 22:57
• A related CV question. Mar 18, 2018 at 9:29
• Obligatory comic to show why it is stupid to just rely on 95% confidence in a world with publishing pressure! Mar 18, 2018 at 14:09

The use of this number in applied statistics can be traced to the influence of Ronald Fisher's classic textbook, Statistical Methods for Research Workers, first published in 1925:

"The value for which P = .05, or 1 in 20, is 1.96 or nearly 2 ; it is convenient to take this point as a limit in judging whether a deviation is to be considered significant or not."

• It is worth noting that Fisher was working at the Rothamsted Experimental Station on crop research, so this $2$ standard deviation rule-of-thumb is likely to be something he had found practically useful Mar 17, 2018 at 21:45

$95\%$ is just the conventionally accepted boundary for "reasonably certain" in general cases. It has nothing to do with any specific formulas, and is rather an arbitrary choice that statisticians have agreed is a good compromise between getting results at all and getting results we can trust.

• Though en.wikipedia.org/wiki/Replication_crisis indicates there might be good reason to shift to higher confidence intervals. Also: xkcd.com/882 Mar 17, 2018 at 20:45
• @DanielSchepler "5% of all the research is actually wrong" is a mind-boglingly huge number, given how many papers are published every day. Imagine an enterprise that has the 1-to-20 ratio of rejects in its production. Mar 18, 2018 at 0:18
• @Joker_vD: I think its even worse than that -- I imagine wrong results are much more likely to be publishable than right ones.
– user14972
Mar 18, 2018 at 1:42
• @Joker_vD : Veritasium did a great YouTube video on this entitled "Why most research is wrong." Worth watching Mar 18, 2018 at 8:56
• @JackAidley There isn't enough incentive in attempting to replicate other studies compared to making new science. Mar 18, 2018 at 14:00

I don't think it is arbitrary because given a normal distribution

68.27% of all values lie within 1 standard deviation
95.45% of all values lie within 2 standard deviations and
99.73% of all values lie within 3 standard deviations

• But the multiples of standard deviation are themselves arbitrary. Mar 18, 2018 at 16:12
• @JeremyC: Indeed. Why not consider the percent of values within $\frac{2154}{1099}$ many standard deviations? Mar 18, 2018 at 16:29