How to do a Wilcoxon signed-rank test (in R)

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Task

Assume we a sample of data, $x_1, x_2, x_3, \ldots x_k$ and either the sample size is small or the population is not normally distributed. But we still want to perform tests that compare the sample median to a hypothesized value (equal, greater, or less). One method is the Wilcoxon Signed-Rank Test.

Related tasks:

• How to do a Kruskal-Wallis test
• How to do a Wilcoxon rank-sum test
• How to do a Wilcoxon signed-rank test for matched pairs

Solution

We’re going to use fake data for illustrative purposes, but you can replace our fake data with your real data. Say our sample, $x_1, x_2, x_3, \ldots x_k$, has median $m$.

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# Replace the next line with your data
sample <- c(19, 4, 23, 16, 1, 8, 30, 25, 13)

We choose a value, $0 \le \alpha \le 1$, as the Type I Error Rate. We’ll let $\alpha$ be 0.05. In the examples below, we will be comparing the median $m$ to a hypothesized value of $a=10$, but you can use any value for $a$.

Two-tailed test

To test the null hypothesis $H_0: m=a$, we use a two-tailed test:

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a <- 10
wilcox.test(sample, mu = a, alternative = "two.sided")

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Warning message in wilcox.test.default(sample, mu = a, alternative = "two.sided"):
“cannot compute exact p-value with ties”




	Wilcoxon signed rank test with continuity correction

data:  sample
V = 35, p-value = 0.1544
alternative hypothesis: true location is not equal to 10

Our p-value, 0.1544, is greater than $\alpha=0.05$, so we do not have sufficient evidence to reject the null hypothesis. We may continue to assume the population median is equal to 10.

Right-tailed test

To test the null hypothesis $H_0: m\ge a$, we use a right-tailed test:

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wilcox.test(sample, mu = a, alternative = "less")

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Warning message in wilcox.test.default(sample, mu = a, alternative = "less"):
“cannot compute exact p-value with ties”




	Wilcoxon signed rank test with continuity correction

data:  sample
V = 35, p-value = 0.9386
alternative hypothesis: true location is less than 10

Our p-value, 0.9386, is greater than $\alpha=0.05$, so we do not have sufficient evidence to reject the null hypothesis. We may continue to assume the population median is less than (or equal to) 10.

Left-tailed test

To test the null hypothesis $H_0: m\le a$, we use a left-tailed test:

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wilcox.test(sample, mu = a, alternative = "greater")

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Warning message in wilcox.test.default(sample, mu = a, alternative = "greater"):
“cannot compute exact p-value with ties”




	Wilcoxon signed rank test with continuity correction

data:  sample
V = 35, p-value = 0.0772
alternative hypothesis: true location is greater than 10

Our p-value, 0.0772, is greater than $\alpha$, so we do not have sufficient evidence to reject the null hypothesis. We may continue to assume the population median is greater than (or equal to) 10.

NOTE: If there are ties in the data and there are fewer than 50 observations in each sample, then R will compute a $p$-value using the normal approximation, and there will be an error message indicating that the exact $p$-value cannot be calculated.

Content last modified on 24 July 2023.

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Contributed by Elizabeth Czarniak (CZARNIA_ELIZ@bentley.edu)