Solved – P value and confidence interval for two sample test of proportions disagree

I'm using R to calculate the two-sample test for equality of proportions, where the two proportions are 350/400 and 25/25. So:

> prop.test(c(350,25),c(400,25))                                                                                                                                                                     2-sample test for equality of proportions with continuity correction  data:  c(350, 25) out of c(400, 25)  X-squared = 2.4399, df = 1, p-value = 0.1183 alternative hypothesis: two.sided  95 percent confidence interval:  -0.17865986 -0.07134014  sample estimates: prop 1 prop 2   0.875  1.000   Warning message: In prop.test(c(350, 25), c(400, 25), correct = FALSE) :   Chi-squared approximation may be incorrect 

What I can't reconcile on my own is that the p-value is greater than 0.05, and yet the 95% confidence interval for the difference does not include 0. I thought there was an 'if and only if' relationship between the two (The p-value < alpha iff the (1-alpha) confidence interval of the difference does not include 0).

What am I not seeing? My only guess is there's something fundamental that I'm misunderstanding, or that it has something to do with that warning message about chi-squared approximation.

I presume they result from two somewhat different approximations in this instance.

For the ordinary chi-square test, the interval that corresponds to the chi-square is the Wilson score interval

$$frac{1}{1 + frac{1}{n} z_{1 – frac{1}{2}alpha}^2} left[ hat p + frac{1}{2n} z_{1 – frac{1}{2}alpha}^2 pm z_{1 – frac{1}{2}alpha} sqrt{ frac{1}{n}hat p left(1 – hat pright) + frac{1}{4n^2}z_{1 – frac{1}{2}alpha}^2 } right]$$

Looking into the code (just type prop.test to see the code for it), it looks like you get the Wilson score interval by default, but with a continuity correction applied to $p$.

[Note that one of the references in the help (?prop.test) discusses eleven different confidence intervals for the difference in proportions; at most one will always exactly correspond to any given form of the hypothesis test.]

While the without-continuity-correction Wilson score interval will correspond to the without-continuity-correction chi-square, my guess is that the continuity-corrected version of both that is being used no longer correspond exactly.

I guess the way to get an interval that should correspond would be to write the interval corresponding to the continuity-corrected chi-squared in similar fashion to the way the Wilson score interval is derived (see the above Wikipedia link) and solve that for the endpoints.

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