When using prop.test()
in R, I noticed that changing my confidence interval does not impact the p value. I use correct = FALSE, conf.level = 0.95, alternative = "two-sided"
as parameters in prop.test()
.
Here, I use 95% CI.
2-sample test for equality of proportions without continuity correction
data: c(332, 307) out of c(382, 333)
X-squared = 5.2235, df = 1, p-value = 0.02228
alternative hypothesis: two.sided
95 percent confidence interval:
-0.097245583 -0.008378365
sample estimates:
prop 1 prop 2
0.8691099 0.9219219
Here, I use 99% CI, however, the p-value is the same:
2-sample test for equality of proportions without continuity correction
data: c(332, 307) out of c(382, 333)
X-squared = 5.2235, df = 1, p-value = 0.02228
alternative hypothesis: two.sided
99 percent confidence interval:
-0.111207635 0.005583686
sample estimates:
prop 1 prop 2
0.8691099 0.9219219
I would presume then the p-value is calculated from the X-squared test statistic, which doesn't incorporate confidence intervals in its calculation? I'd love clarification on this. The similar posts I found on StackExchange included a warning error in the output about the chi-square test possibly being incorrect, which my output did not include.
My second question would be that the 99% CI now includes 0. How, then, can we have a p value < 0.05?