# Can I get a p-value testing statistical significance between two coin flipping rounds?

i'm a graduate student (biology program) with minimal experience with statistics. I have two questions relating to my project that i'd love help with! The first question is how I can take a single measurement of many events (such as, 55 coin flips out of 100 yielded heads) and test if that is significantly different than a separate measurement (such as, now with a different coin, I see 40 coin flips out of 100 yields heads - so are the two coins significantly different? Can I put a p-value on that?). The second question is, if I use an R program that draws a best fit line on some data and gives i.e. the slope is 10 and the 95% confidence interval is from 8 to 12, then compare that to another slope that is 14 with a 95% CI from 12 to 16, are these results significantly different? And can I put a p-value on that as well? Thanks in advance for any help!

Let's do the coin flip first. Here is how to do this in R

heads<-c(55,40)
flips<-c(100,100)


The heads array houses the number of heads we observed. The flips array houses the number of times each coin was flipped. The function prop.test performs a test of proportions. You can see the p-value in the print out.
• Am I missing something? OP asked about 55 vs 40 each out of 100. Then from prop.test(c(55,40),c(100,100), correct = F)\$p.val, I get 0.03367207, so signif diff at 5% level. Sep 4, 2019 at 18:25