Let’s take a quick look at Abbey’s data. It shows that successful birds have more urban areas than non-successful birds.

## # A tibble: 2 × 3
##   status   mean    sd
##   <fct>   <dbl> <dbl>
## 1 failed   6.21  11.0
## 2 success  9.93  16.5

If we do a statistical comparison, we see that there is statistically no difference.

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  urban by status
## W = 1227, p-value = 0.5721
## alternative hypothesis: true location shift is not equal to 0

What about Cassidy’s data?

## # A tibble: 2 × 3
##   status   mean    sd
##   <chr>   <dbl> <dbl>
## 1 failed   14.0  21.8
## 2 success  10.4  15.8

Her data shows that failed birds have more urban area, but it’s also not statistically significant, like Abbey’s.

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  urban by status
## W = 2630, p-value = 0.266
## alternative hypothesis: true location shift is not equal to 0

But why is this? Even though they both show no statistical significance, why are we getting different patterns?

Let’s first look at Abbey’s data. This is what her graph looks like.

Now, if we only take the birds that Cassidy also uses, we see this change. That takes Abbey from 108 birds to 94.

Her success column goes down a lot. She has 14 extra birds that Cassidy doesn’t use, I’m assuming due to brood rearing drone data. Let’s look at those 14 birds.

The successful birds that are in Abbey’s data set, but not mine, have a LOT of urban area. I’m “missing” these birds from my data set.

Great, so what if I add those birds to my data set?

When we look at my numbers overall, we see that now, successful birds have more urban area.

## # A tibble: 2 × 3
##   status   mean    sd
##   <fct>   <dbl> <dbl>
## 1 failed   11.0  19.9
## 2 success  12.3  17.7

Again, here’s Abbey’s original numbers. It seems like my method is estimating the amount in general to be higher.

## # A tibble: 2 × 3
##   status   mean    sd
##   <fct>   <dbl> <dbl>
## 1 failed   6.21  11.0
## 2 success  9.93  16.5