Dear R team, I'm not sure if I use the right distribution list, but I hope in case if not, you will forward it to the reference person. Following problem occured: I used R to calculate the p-value for the two sided binomial test (exact - Pearson). For a very little difference for my forecast I get a very big difference in my p-value > binom.test(1,101, 0.02402)
Exact binomial test data: 1 and 101 number of successes = 1, number of trials = 101, p-value = 0.7375 alternative hypothesis: true probability of success is not equal to 0.02402 95 percent confidence interval: 0.00025064 0.05393235 sample estimates: probability of success 0.00990099 > binom.test(1,101, 0.02403) Exact binomial test data: 1 and 101 number of successes = 1, number of trials = 101, p-value = 0.5243 alternative hypothesis: true probability of success is not equal to 0.02403 95 percent confidence interval: 0.00025064 0.05393235 sample estimates: probability of success 0.00990099 Can you please explain where this huge difference come from? Which mathematical explanation is given for this topic? Please help. I hope for your soon feedback. Kind Regards, Alwina ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
