I am trying to perform Wilcox test in R. For the simplicity I will use artificial data in example below:

# Weight before experiment
before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7)

# Weight after experiment
    after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2)
 # Create a data frame
        my_data <- data.frame( 
                        group = rep(c("before", "after"), each = 10),
                        weight = c(before,  after)

So next step is performing Wilcox test. I want to test test Ho-hypothesis which mean data before and after experiment have same median, or alternative Ha: Sample after experiment have big value than sample before experiment.

In order to do this I perform this test below :

wilcox.test(weight ~ group, data = my_data, paired = TRUE,
              alternative = "less")

#Wilcoxon signed rank test

#data:  weight by group
#V = 55, p-value = 1
#alternative hypothesis: true location shift is less than 0

So can anybody help me with interpretation of this results ? First does my hypothesis is good and second what will be explanation because value of P is exactly 1 ?


In your example, everyone gained weight and you tested the hypothesis that everyone lost weight (alternative = "less"), so it VERY VERY UNLIKELY that the participants lost weight, so $p=1$. Try instead "greater" or "two-sided".

> wilcox.test(weight ~ group, data = my_data, paired = TRUE,
+             alternative = "greater")

    Wilcoxon signed rank exact test

data:  weight by group
V = 55, p-value = 0.0009766
alternative hypothesis: true location shift is greater than 0

Now, you have a significant result, $p < .001$, so you can reject the null hypothesis : it is likely that participants gained weight.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.