I have a dataset with birds that were exposed to pesticides. The summary of this data looks like:
Total number tested Age1 Age2 Age3 Total Male 28 34 60 122 Female 16 28 50 94 Total 44 62 110 216 Total Number Tested Positive Age1 Age2 Age3 Total Male 15 23 34 72 Female 11 25 35 71 Total 26 48 69 143
The questions I'm trying to answer are:
- Is age class2 more exposed to pesticides than the other 2 age classes?
- Are males more exposed than females?
It seems like it should be a simple fisher test, but I'm not sure if I'm applying the right test and interpreting it right. Which test would it be most appropriate to apply to answer those questions? Is there a way to include all 3 age classes at once, or I should do it pairwise?
I tried this:
Hypothesis Age1 < Age2
fisher.test(rbind(c(26,44-26), c(48,62-48)), alternative="less") Fisher's Exact Test for Count Data data: rbind(c(26, 44 - 26), c(48, 62 - 48)) p-value = 0.03549 alternative hypothesis: true odds ratio is less than 1 95 percent confidence interval: 0.0000000 0.9344918 sample estimates: odds ratio 0.4249067
p<0.05 => reject the null hypothesis, so this means there is no difference between Age1 and Age2 or am I getting it wrong?