I'm running a paired t-test to compare before and after counts. Here's what my data looks like:
before <- c(102,122,64,0) after <- c(38,39,23,16)
Here's my results of the t.test:
t.test(before, after, data = fence2016.data, paired = TRUE) Paired t-test data: before and after t = 2.0038, df = 3, p-value = 0.1388 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -25.29295 111.29295 sample estimates: mean of the differences 43
I'm wondering why my p-value isn't that significant? The count data obviously changes significantly between before and after. Is there another parameter I should be using? I want to prove that there is a significant difference between before and after (hopefully a negative difference too).