Background
My friend did an observational experiment. She observed several colonies of animals for six months (one time per month) in the year 2014 and 2015. Some treatment to the environments of those animals were made in the year 2014 and in the year 2015, the environment was kept as natural as possible. She wants to test if there is a statistically significant difference between the sizes of colonies comparing the year 2014 and 2015.
The situation
The friend wants to use Wilcoxson's test for paired data to compare if differences in colony size among the year 2014 and 2015 are statistically significant. Each observation is the size of the colony in each month. In my opinion, the test is not appropriate as data is time series.
The plots of the results
The question
- Is Wilcoxon's test appropriate for this problem?
- If not, could you advise what are the appropriate methods (if possible, an example in
R
would be appreciated)? - Are any methods of inferential statistics appropriate at all?
The data and R code for plots
library(data.table)
library(ggplot2)
col1 <- fread("
colony_size year month
637 y2014 01
609 y2014 02
582 y2014 03
902 y2014 04
945 y2014 05
954 y2014 06
640 y2015 01
615 y2015 02
585 y2015 03
922 y2015 04
1040 y2015 05
1097 y2015 06 ")
ggplot(col1, aes(month, colony_size, color = year)) +
geom_line() + geom_point() + theme_bw() + ggtitle("Colony 1")