NonParametric Tests Homework Examples
Nonparametric Tests
Author:
Lisa Sullivan, PhD
Professor of Biostatistics
Boston University School of Public Health
Introduction
The three modules on hypothesis testing presented a number of tests of hypothesis for continuous, dichotomous and discrete outcomes. Tests for continuous outcomes focused on comparing means, while tests for dichotomous and discrete outcomes focused on comparing proportions. All of the tests presented in the modules on hypothesis testing are called parametric tests and are based on certain assumptions. For example, when running tests of hypothesis for means of continuous outcomes, all parametric tests assume that the outcome is approximately normally distributed in the population. This does not mean that the data in the observed sample follows a normal distribution, but rather that the outcome follows a normal distribution in the full population which is not observed. For many outcomes, investigators are comfortable with the normality assumption (i.e., most of the observations are in the center of the distribution while fewer are at either extreme). It also turns out that many statistical tests are robust, which means that they maintain their statistical properties even when assumptions are not entirely met. Tests are robust in the presence of violations of the normality assumption when the sample size is large based on the Central Limit Theorem (see page 11 in the module on Probability). When the sample size is small and the distribution of the outcome is not known and cannot be assumed to be approximately normally distributed, then alternative tests called nonparametric tests are appropriate.
Learning Objectives
After completing this module, the student will be able to:
 Compare and contrast parametric and nonparametric tests
 Identify multiple applications where nonparametric approaches are appropriate
 Perform and interpret the Mann Whitney U Test
 Perform and interpret the Sign test and Wilcoxon Signed Rank Test
 Compare and contrast the Sign test and Wilcoxon Signed Rank Test
 Perform and interpret the Kruskal Wallis test
 Identify the appropriate nonparametric hypothesis testing procedure based on type of outcome variable and number of samples
When to Use a Nonparametric Test
Nonparametric tests are sometimes called distributionfree tests because they are based on fewer assumptions (e.g., they do not assume that the outcome is approximately normally distributed). Parametric tests involve specific probability distributions (e.g., the normal distribution) and the tests involve estimation of the key parameters of that distribution (e.g., the mean or difference in means) from the sample data. The cost of fewer assumptions is that nonparametric tests are generally less powerful than their parametric counterparts (i.e., when the alternative is true, they may be less likely to reject H_{0}).
It can sometimes be difficult to assess whether a continuous outcome follows a normal distribution and, thus, whether a parametric or nonparametric test is appropriate. There are several statistical tests that can be used to assess whether data are likely from a normal distribution. The most popular are the KolmogorovSmirnov test, the AndersonDarling test, and the ShapiroWilk test^{1}. Each test is essentially a goodness of fit test and compares observed data to quantiles of the normal (or other specified) distribution. The null hypothesis for each test is H_{0}: Data follow a normal distribution versus H_{1}: Data do not follow a normal distribution. If the test is statistically significant (e.g., p<0.05), then data do not follow a normal distribution, and a nonparametric test is warranted. It should be noted that these tests for normality can be subject to low power. Specifically, the tests may fail to reject H_{0}: Data follow a normal distribution when in fact the data do not follow a normal distribution. Low power is a major issue when the sample size is small  which unfortunately is often when we wish to employ these tests. The most practical approach to assessing normality involves investigating the distributional form of the outcome in the sample using a histogram and to augment that with data from other studies, if available, that may indicate the likely distribution of the outcome in the population.
There are some situations when it is clear that the outcome does not follow a normal distribution. These include situations:
 when the outcome is an ordinal variable or a rank,
 when there are definite outliers or
 when the outcome has clear limits of detection.
Using an Ordinal Scale
Consider a clinical trial where study participants are asked to rate their symptom severity following 6 weeks on the assigned treatment. Symptom severity might be measured on a 5 point ordinal scale with response options: Symptoms got much worse, slightly worse, no change, slightly improved, or much improved. Suppose there are a total of n=20 participants in the trial, randomized to an experimental treatment or placebo, and the outcome data are distributed as shown in the figure below.
Distribution of Symptom Severity in Total Sample
The distribution of the outcome (symptom severity) does not appear to be normal as more participants report improvement in symptoms as opposed to worsening of symptoms.
When the Outcome is a Rank
In some studies, the outcome is a rank. For example, in obstetrical studies an APGAR score is often used to assess the health of a newborn. The score, which ranges from 110, is the sum of five component scores based on the infant's condition at birth. APGAR scores generally do not follow a normal distribution, since most newborns have scores of 7 or higher (normal range).
When There Are Outliers
In some studies, the outcome is continuous but subject to outliers or extreme values. For example, days in the hospital following a particular surgical procedure is an outcome that is often subject to outliers. Suppose in an observational study investigators wish to assess whether there is a difference in the days patients spend in the hospital following liver transplant in forprofit versus nonprofit hospitals. Suppose we measure days in the hospital following transplant in n=100 participants, 50 from forprofit and 50 from nonprofit hospitals. The number of days in the hospital are summarized by the boxwhisker plot below.
Distribution of Days in the Hospital Following Transplant
Note that 75% of the participants stay at most 16 days in the hospital following transplant, while at least 1 stays 35 days which would be considered an outlier. Recall from page 8 in the module on Summarizing Data that we used Q_{1}1.5(Q_{3}Q_{1}) as a lower limit and Q_{3}+1.5(Q_{3}Q_{1}) as an upper limit to detect outliers. In the boxwhisker plot above, 10.2, Q_{1}=12 and Q_{3}=16, thus outliers are values below 121.5(1612) = 6 or above 16+1.5(1612) = 22.
Limits of Detection
In some studies, the outcome is a continuous variable that is measured with some imprecision (e.g., with clear limits of detection). For example, some instruments or assays cannot measure presence of specific quantities above or below certain limits. HIV viral load is a measure of the amount of virus in the body and is measured as the amount of virus per a certain volume of blood. It can range from "not detected" or "below the limit of detection" to hundreds of millions of copies. Thus, in a sample some participants may have measures like 1,254,000 or 874,050 copies and others are measured as "not detected." If a substantial number of participants have undetectable levels, the distribution of viral load is not normally distributed.
Hypothesis Testing with Nonparametric Tests In nonparametric tests, the hypotheses are not about population parameters (e.g., μ=50 or μ_{1}=μ_{2}). Instead, the null hypothesis is more general. For example, when comparing two independent groups in terms of a continuous outcome, the null hypothesis in a parametric test is H_{0}: μ_{1} =μ_{2}. In a nonparametric test the null hypothesis is that the two populations are equal, often this is interpreted as the two populations are equal in terms of their central tendency.

Advantages of Nonparametric Tests
Nonparametric tests have some distinct advantages. With outcomes such as those described above, nonparametric tests may be the only way to analyze these data. Outcomes that are ordinal, ranked, subject to outliers or measured imprecisely are difficult to analyze with parametric methods without making major assumptions about their distributions as well as decisions about coding some values (e.g., "not detected"). As described here, nonparametric tests can also be relatively simple to conduct.
Introduction to Nonparametric Testing
This module will describe some popular nonparametric tests for continuous outcomes. Interested readers should see Conover^{3} for a more comprehensive coverage of nonparametric tests.
Key Concept:
Parametric tests are generally more powerful and can test a wider range of alternative hypotheses. It is worth repeating that if data are approximately normally distributed then parametric tests (as in the modules on hypothesis testing) are more appropriate. However, there are situations in which assumptions for a parametric test are violated and a nonparametric test is more appropriate. 
The techniques described here apply to outcomes that are ordinal, ranked, or continuous outcome variables that are not normally distributed. Recall that continuous outcomes are quantitative measures based on a specific measurement scale (e.g., weight in pounds, height in inches). Some investigators make the distinction between continuous, interval and ordinal scaled data. Interval data are like continuous data in that they are measured on a constant scale (i.e., there exists the same difference between adjacent scale scores across the entire spectrum of scores). Differences between interval scores are interpretable, but ratios are not. Temperature in Celsius or Fahrenheit is an example of an interval scale outcome. The difference between 30º and 40º is the same as the difference between 70º and 80º, yet 80º is not twice as warm as 40º. Ordinal outcomes can be less specific as the ordered categories need not be equally spaced. Symptom severity is an example of an ordinal outcome and it is not clear whether the difference between much worse and slightly worse is the same as the difference between no change and slightly improved. Some studies use visual scales to assess participants' selfreported signs and symptoms. Pain is often measured in this way, from 0 to 10 with 0 representing no pain and 10 representing agonizing pain. Participants are sometimes shown a visual scale such as that shown in the upper portion of the figure below and asked to choose the number that best represents their pain state. Sometimes pain scales use visual anchors as shown in the lower portion of the figure below.
Visual Pain Scale
In the upper portion of the figure, certainly 10 is worse than 9, which is worse than 8; however, the difference between adjacent scores may not necessarily be the same. It is important to understand how outcomes are measured to make appropriate inferences based on statistical analysis and, in particular, not to overstate precision.
Assigning Ranks
The nonparametric procedures that we describe here follow the same general procedure. The outcome variable (ordinal, interval or continuous) is ranked from lowest to highest and the analysis focuses on the ranks as opposed to the measured or raw values. For example, suppose we measure selfreported pain using a visual analog scale with anchors at 0 (no pain) and 10 (agonizing pain) and record the following in a sample of n=6 participants:
7 5 9 3 0 2
The ranks, which are used to perform a nonparametric test, are assigned as follows: First, the data are ordered from smallest to largest. The lowest value is then assigned a rank of 1, the next lowest a rank of 2 and so on. The largest value is assigned a rank of n (in this example, n=6). The observed data and corresponding ranks are shown below:
Ordered Observed Data:  0  2  3  5  7  9 
Ranks:  1  2  3  4  5  6 
A complicating issue that arises when assigning ranks occurs when there are ties in the sample (i.e., the same values are measured in two or more participants). For example, suppose that the following data are observed in our sample of n=6:
Observed Data: 7 7 9 3 0 2
The 4^{th} and 5^{th} ordered values are both equal to 7. When assigning ranks, the recommended procedure is to assign the mean rank of 4.5 to each (i.e. the mean of 4 and 5), as follows:
Ordered Observed Data:  0.5  2.5  3.5  7  7  9 
Ranks:  1.5  2.5  3.5  4.5  4.5  6 
Suppose that there are three values of 7. In this case, we assign a rank of 5 (the mean of 4, 5 and 6) to the 4^{th}, 5^{th} and 6^{th} values, as follows:
Ordered Observed Data:  0  2  3  7  7  7 
Ranks:  1  2  3  5  5  5 
Using this approach of assigning the mean rank when there are ties ensures that the sum of the ranks is the same in each sample (for example, 1+2+3+4+5+6=21, 1+2+3+4.5+4.5+6=21 and 1+2+3+5+5+5=21). Using this approach, the sum of the ranks will always equal n(n+1)/2. When conducting nonparametric tests, it is useful to check the sum of the ranks before proceeding with the analysis.
To conduct nonparametric tests, we again follow the fivestep approach outlined in the modules on hypothesis testing.
 Set up hypotheses and select the level of significance α. Analogous to parametric testing, the research hypothesis can be one or two sided (one or twotailed), depending on the research question of interest.
 Select the appropriate test statistic. The test statistic is a single number that summarizes the sample information. In nonparametric tests, the observed data is converted into ranks and then the ranks are summarized into a test statistic.
 Set up decision rule. The decision rule is a statement that tells under what circumstances to reject the null hypothesis. Note that in some nonparametric tests we reject H_{0} if the test statistic is large, while in others we reject H_{0} if the test statistic is small. We make the distinction as we describe the different tests.
 Compute the test statistic. Here we compute the test statistic by summarizing the ranks into the test statistic identified in Step 2.
 Conclusion. The final conclusion is made by comparing the test statistic (which is a summary of the information observed in the sample) to the decision rule. The final conclusion is either to reject the null hypothesis (because it is very unlikely to observe the sample data if the null hypothesis is true) or not to reject the null hypothesis (because the sample data are not very unlikely if the null hypothesis is true).
Mann Whitney U Test (Wilcoxon Rank Sum Test)
The modules on hypothesis testing presented techniques for testing the equality of means in two independent samples. An underlying assumption for appropriate use of the tests described was that the continuous outcome was approximately normally distributed or that the samples were sufficiently large (usually n_{1}> 30 and n_{2}> 30) to justify their use based on the Central Limit Theorem. When comparing two independent samples when the outcome is not normally distributed and the samples are small, a nonparametric test is appropriate.
A popular nonparametric test to compare outcomes between two independent groups is the Mann Whitney U test. The Mann Whitney U test, sometimes called the Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is used to test whether two samples are likely to derive from the same population (i.e., that the two populations have the same shape). Some investigators interpret this test as comparing the medians between the two populations. Recall that the parametric test compares the means (H_{0}: μ_{1}=μ_{2}) between independent groups.
In contrast, the null and twosided research hypotheses for the nonparametric test are stated as follows:
H_{0}: The two populations are equal versus
H_{1}: The two populations are not equal.
This test is often performed as a twosided test and, thus, the research hypothesis indicates that the populations are not equal as opposed to specifying directionality. A onesided research hypothesis is used if interest lies in detecting a positive or negative shift in one population as compared to the other. The procedure for the test involves pooling the observations from the two samples into one combined sample, keeping track of which sample each observation comes from, and then ranking lowest to highest from 1 to n_{1}+n_{2}, respectively.
Example:
Consider a Phase II clinical trial designed to investigate the effectiveness of a new drug to reduce symptoms of asthma in children. A total of n=10 participants are randomized to receive either the new drug or a placebo. Participants are asked to record the number of episodes of shortness of breath over a 1 week period following receipt of the assigned treatment. The data are shown below.
Placebo  7  5  6  4  12 
New Drug  3  6  4  2  1 
Is there a difference in the number of episodes of shortness of breath over a 1 week period in participants receiving the new drug as compared to those receiving the placebo? By inspection, it appears that participants receiving the placebo have more episodes of shortness of breath, but is this statistically significant?
In this example, the outcome is a count and in this sample the data do not follow a normal distribution.
Frequency Histogram of Number of Episodes of Shortness of Breath
In addition, the sample size is small (n_{1}=n_{2}=5), so a nonparametric test is appropriate. The hypothesis is given below, and we run the test at the 5% level of significance (i.e., α=0.05).
H_{0}: The two populations are equal versus
H_{1}: The two populations are not equal.
Note that if the null hypothesis is true (i.e., the two populations are equal), we expect to see similar numbers of episodes of shortness of breath in each of the two treatment groups, and we would expect to see some participants reporting few episodes and some reporting more episodes in each group. This does not appear to be the case with the observed data. A test of hypothesis is needed to determine whether the observed data is evidence of a statistically significant difference in populations.
The first step is to assign ranks and to do so we order the data from smallest to largest. This is done on the combined or total sample (i.e., pooling the data from the two treatment groups (n=10)), and assigning ranks from 1 to 10, as follows. We also need to keep track of the group assignments in the total sample.

 Total Sample (Ordered Smallest to Largest)  Ranks  

Placebo  New Drug  Placebo  New Drug  Placebo  New Drug 
7  3 
 1 
 1 
5  6 
 2 
 2 
6  4 
 3 
 3 
4  2  4  4  4.5  4.5 
12  1  5 
 6 


 6  6  7.5  7.5 

 7 
 9 


 12 
 10 

Note that the lower ranks (e.g., 1, 2 and 3) are assigned to responses in the new drug group while the higher ranks (e.g., 9, 10) are assigned to responses in the placebo group. Again, the goal of the test is to determine whether the observed data support a difference in the populations of responses. Recall that in parametric tests (discussed in the modules on hypothesis testing), when comparing means between two groups, we analyzed the difference in the sample means relative to their variability and summarized the sample information in a test statistic. A similar approach is employed here. Specifically, we produce a test statistic based on the ranks.
First, we sum the ranks in each group. In the placebo group, the sum of the ranks is 37; in the new drug group, the sum of the ranks is 18. Recall that the sum of the ranks will always equal n(n+1)/2. As a check on our assignment of ranks, we have n(n+1)/2 = 10(11)/2=55 which is equal to 37+18 = 55.
For the test, we call the placebo group 1 and the new drug group 2 (assignment of groups 1 and 2 is arbitrary). We let R_{1} denote the sum of the ranks in group 1 (i.e., R_{1}=37), and R_{2} denote the sum of the ranks in group 2 (i.e., R_{2}=18). If the null hypothesis is true (i.e., if the two populations are equal), we expect R_{1} and R_{2} to be similar. In this example, the lower values (lower ranks) are clustered in the new drug group (group 2), while the higher values (higher ranks) are clustered in the placebo group (group 1). This is suggestive, but is the observed difference in the sums of the ranks simply due to chance? To answer this we will compute a test statistic to summarize the sample information and look up the corresponding value in a probability distribution.
Test Statistic for the Mann Whitney U Test
The test statistic for the Mann Whitney U Test is denoted U and is the smaller of U_{1} and U_{2}, defined below.
where R_{1} = sum of the ranks for group 1 and R_{2} = sum of the ranks for group 2.
For this example,
In our example, U=3. Is this evidence in support of the null or research hypothesis? Before we address this question, we consider the range of the test statistic U in two different situations.
Situation #1
Consider the situation where there is complete separation of the groups, supporting the research hypothesis that the two populations are not equal. If all of the higher numbers of episodes of shortness of breath (and thus all of the higher ranks) are in the placebo group, and all of the lower numbers of episodes (and ranks) are in the new drug group and that there are no ties, then:
and
Therefore, when there is clearly a difference in the populations, U=0.
Situation #2
Consider a second situation where low and high scores are approximately evenly distributed in the two groups, supporting the null hypothesis that the groups are equal. If ranks of 2, 4, 6, 8 and 10 are assigned to the numbers of episodes of shortness of breath reported in the placebo group and ranks of 1, 3, 5, 7 and 9 are assigned to the numbers of episodes of shortness of breath reported in the new drug group, then:
R_{1}= 2+4+6+8+10 = 30 and R_{2}= 1+3+5+7+9 = 25,
and
When there is clearly no difference between populations, then U=10.
Thus, smaller values of U support the research hypothesis, and larger values of U support the null hypothesis.
Key Concept: For any MannWhitney U test, the theoretical range of U is from 0 (complete separation between groups, H_{0} most likely false and H_{1} most likely true) to n_{1}*n_{2} (little evidence in support of H_{1}).
In every test, U_{1}+U_{2 } is always equal to n_{1}*n_{2}. In the example above, U can range from 0 to 25 and smaller values of U support the research hypothesis (i.e., we reject H_{0} if U is small). The procedure for determining exactly when to reject H_{0} is described below. 
In every test, we must determine whether the observed U supports the null or research hypothesis. This is done following the same approach used in parametric testing. Specifically, we determine a critical value of U such that if the observed value of U is less than or equal to the critical value, we reject H_{0} in favor of H_{1} and if the observed value of U exceeds the critical value we do not reject H_{0}.
The critical value of U can be found in the table below. To determine the appropriate critical value we need sample sizes (for Example: n_{1}=n_{2}=5) and our twosided level of significance (α=0.05). For Example 1 the critical value is 2, and the decision rule is to reject H_{0} if U < 2. We do not reject H_{0} because 3 > 2. We do not have statistically significant evidence at α =0.05, to show that the two populations of numbers of episodes of shortness of breath are not equal. However, in this example, the failure to reach statistical significance may be due to low power. The sample data suggest a difference, but the sample sizes are too small to conclude that there is a statistically significant difference.
Table of Critical Values for U
Example:
A new approach to prenatal care is proposed for pregnant women living in a rural community. The new program involves inhome visits during the course of pregnancy in addition to the usual or regularly scheduled visits. A pilot randomized trial with 15 pregnant women is designed to evaluate whether women who participate in the program deliver healthier babies than women receiving usual care. The outcome is the APGAR score measured 5 minutes after birth. Recall that APGAR scores range from 0 to 10 with scores of 7 or higher considered normal (healthy), 46 low and 03 critically low. The data are shown below.
Usual Care  8  7  6  2  5  8  7  3 
New Program  9  9  7  8  10  9  6 

Is there statistical evidence of a difference in APGAR scores in women receiving the new and enhanced versus usual prenatal care? We run the test using the fivestep approach.
 Step 1. Set up hypotheses and determine level of significance.
H_{0}: The two populations are equal versus
H_{1}: The two populations are not equal. α =0.05
 Step 2. Select the appropriate test statistic.
Because APGAR scores are not normally distributed and the samples are small (n_{1}=8 and n_{2}=7), we use the Mann Whitney U test. The test statistic is U, the smaller of
where R_{1} and R_{2} are the sums of the ranks in groups 1 and 2, respectively.
 Step 3. Set up decision rule.
The appropriate critical value can be found in the table above. To determine the appropriate critical value we need sample sizes (n_{1}=8 and n_{2}=7) and our twosided level of significance (α=0.05). The critical value for this test with n_{1}=8, n_{2}=7 and α =0.05 is 10 and the decision rule is as follows: Reject H_{0} if U < 10.
 Step 4. Compute the test statistic.
The first step is to assign ranks of 1 through 15 to the smallest through largest values in the total sample, as follows:

 Total Sample (Ordered Smallest to Largest)  Ranks  

Usual Care  New Program  Usual Care  New Program  Usual Care  New Program 
8  9  2 
 1 

7  8  3 
 2 

6  7  5 
 3 

2  8  6  6  4.5  4.5 
5  10  7  7  7  7 
8  9  7 
 7 

7  6  8  8  10.5  10.5 
3 
 8  8  10.5  10.5 


 9 
 13.5 


 9 
 13.5 


 10 
 15 



 R_{1}=45.5  R_{2}=74.5 
Next, we sum the ranks in each group. In the usual care group, the sum of the ranks is R_{1}=45.5 and in the new program group, the sum of the ranks is R_{2}=74.5. Recall that the sum of the ranks will always equal n(n+1)/2. As a check on our assignment of ranks, we have n(n+1)/2 = 15(16)/2=120 which is equal to 45.5+74.5 = 120.
We now compute U_{1} and U_{2}, as follows:
Thus, the test statistic is U=9.5.
 Step 5. Conclusion:
We reject H_{0} because 9.5 < 10. We have statistically significant evidence at α =0.05 to show that the populations of APGAR scores are not equal in women receiving usual prenatal care as compared to the new program of prenatal care.
Example:
A clinical trial is run to assess the effectiveness of a new antiretroviral therapy for patients with HIV. Patients are randomized to receive a standard antiretroviral therapy (usual care) or the new antiretroviral therapy and are monitored for 3 months. The primary outcome is viral load which represents the number of HIV copies per milliliter of blood. A total of 30 participants are randomized and the data are shown below.
Standard Therapy  7500  8000  2000  550  1250  1000  2250  6800  3400  6300  9100  970  1040  670  400 
New Therapy  400  250  800  1400  8000  7400  1020  6000  920  1420  2700  4200  5200  4100  undetectable 
Is there statistical evidence of a difference in viral load in patients receiving the standard versus the new antiretroviral therapy?
 Step 1. Set up hypotheses and determine level of significance.
H_{0}: The two populations are equal versus
H_{1}: The two populations are not equal. α=0.05
 Step 2. Select the appropriate test statistic.
Because viral load measures are not normally distributed (with outliers as well as limits of detection (e.g., "undetectable")), we use the MannWhitney U test. The test statistic is U, the smaller of
where R_{1} and R_{2} are the sums of the ranks in groups 1 and 2, respectively.
 Step 3. Set up the decision rule.
The critical value can be found in the table of critical values based on sample sizes (n_{1}=n_{2}=15) and a twosided level of significance (α=0.05). The critical value 64 and the decision rule is as follows: Reject H_{0} if U < 64.
 Step 4. Compute the test statistic.
The first step is to assign ranks of 1 through 30 to the smallest through largest values in the total sample. Note in the table below, that the "undetectable" measurement is listed first in the ordered values (smallest) and assigned a rank of 1.

 Total Sample (Ordered Smallest to Largest)  Ranks  

Standard Antiretroviral  New Antiretroviral  Standard Antiretroviral  New Antiretroviral  Standard Antiretroviral  New Antiretroviral 
7500  400 
 undetectable 
 1 
8000  250 
 250 
 2 
2000  800  400  400  3.5  3.5 
550  1400  550 
 5 

1250  8000  670 
 6 

1000  7400 
 800 
 7 
2250  1020 
 920 
 8 
6800  6000  970 
 9 

3400  920  1000 
 10 

6300  1420 
 1020 
 11 
9100  2700  1040 
 12 

970  4200  1250 
 13 

1040  5200 
 1400 
 14 
670  4100 
 1420 
 15 
400  undetectable  2000 
 16 


 2250 
 17 



 2700 
 18 

 3400 
 19 



 4100 
 20 


 4200 
 21 


 5200 
 22 


 6000 
 23 

 6300 
 24 


 6800 
 25 



 7400 
 26 

 7500 
 27 


 8000  8000  28.5  28.5 

 9100 
 30 




 R_{1} = 245  R_{2} = 220 
Next, we sum the ranks in each group. In the standard antiretroviral therapy group, the sum of the ranks is R_{1}=245; in the new antiretroviral therapy group, the sum of the ranks is R_{2}=220. Recall that the sum of the ranks will always equal n(n+1)/2. As a check on our assignment of ranks, we have n(n+1)/2 = 30(31)/2=465 which is equal to 245+220 = 465. We now compute U_{1} and U_{2}, as follows,
Thus, the test statistic is U=100.
 Step 5. Conclusion.
We do not reject H_{0} because 100 > 64. We do not have sufficient evidence to conclude that the treatment groups differ in viral load.
Tests with Matched Samples
This section describes nonparametric tests to compare two groups with respect to a continuous outcome when the data are collected on matched or paired samples. The parametric procedure for doing this was presented in the modules on hypothesis testing for the situation in which the continuous outcome was normally distributed. This section describes procedures that should be used when the outcome cannot be assumed to follow a normal distribution. There are two popular nonparametric tests to compare outcomes between two matched or paired groups. The first is called the Sign Test and the second the Wilcoxon Signed Rank Test.
Recall that when data are matched or paired, we compute difference scores for each individual and analyze difference scores. The same approach is followed in nonparametric tests. In parametric tests, the null hypothesis is that the mean difference (μ_{d}) is zero. In nonparametric tests, the null hypothesis is that the median difference is zero.
Example:
Consider a clinical investigation to assess the effectiveness of a new drug designed to reduce repetitive behaviors in children affected with autism. If the drug is effective, children will exhibit fewer repetitive behaviors on treatment as compared to when they are untreated. A total of 8 children with autism enroll in the study. Each child is observed by the study psychologist for a period of 3 hours both before treatment and then again after taking the new drug for 1 week. The time that each child is engaged in repetitive behavior during each 3 hour observation period is measured. Repetitive behavior is scored on a scale of 0 to 100 and scores represent the percent of the observation time in which the child is engaged in repetitive behavior. For example, a score of 0 indicates that during the entire observation period the child did not engage in repetitive behavior while a score of 100 indicates that the child was constantly engaged in repetitive behavior. The data are shown below.
Child  Before Treatment  After 1 Week of Treatment 

1  85  75 
2  70  50 
3  40  50 
4  65  40 
5  80  20 
6  75  65 
7  55  40 
8  20  25 
Looking at the data, it appears that some children improve (e.g., Child 5 scored 80 before treatment and 20 after treatment), but some got worse (e.g., Child 3 scored 40 before treatment and 50 after treatment). Is there statistically significant improvement in repetitive behavior after 1 week of treatment?.
Because the before and after treatment measures are paired, we compute difference scores for each child. In this example, we subtract the assessment of repetitive behaviors after treatment from that measured before treatment so that difference scores represent improvement in repetitive behavior. The question of interest is whether there is significant improvement after treatment.
Child  Before Treatment  After 1 Week of Treatment  Difference (BeforeAfter) 

1  85  75  10 
2  70  50  20 
3  40  50  10 
4  65  40  25 
5  80  20  60 
6  75  65  10 
7  55  40  15 
8  20  25  5 
In this small sample, the observed difference (or improvement) scores vary widely and are subject to extremes (e.g., the observed difference of 60 is an outlier). Thus, a nonparametric test is appropriate to test whether there is significant improvement in repetitive behavior before versus after treatment. The hypotheses are given below.
H_{0}: The median difference is zero versus
H_{1}: The median difference is positive α=0.05
In this example, the null hypothesis is that there is no difference in scores before versus after treatment. If the null hypothesis is true, we expect to see some positive differences (improvement) and some negative differences (worsening). If the research hypothesis is true, we expect to see more positive differences after treatment as compared to before.
The Sign Test
The Sign Test is the simplest nonparametric test for matched or paired data. The approach is to analyze only the signs of the difference scores, as shown below:
Child  Before Treatment  After 1 Week of Treatment  Difference (BeforeAfter)  Sign 

1  85  75  10  + 
2  70  50  20  + 
3  40  50  10   
4  65  40  25  + 
5  80  20  60  + 
6  75  65  10  + 
7  55  40  15  + 
8  20  25  5   
If the null hypothesis is true (i.e., if the median difference is zero) then we expect to see approximately half of the differences as positive and half of the differences as negative. If the research hypothesis is true, we expect to see more positive differences.
Test Statistic for the Sign Test
The test statistic for the Sign Test is the number of positive signs or number of negative signs, whichever is smaller. In this example, we observe 2 negative and 6 positive signs. Is this evidence of significant improvement or simply due to chance?
Determining whether the observed test statistic supports the null or research hypothesis is done following the same approach used in parametric testing. Specifically, we determine a critical value such that if the smaller of the number of positive or negative signs is less than or equal to that critical value, then we reject H_{0} in favor of H_{1} and if the smaller of the number of positive or negative signs is greater than the critical value, then we do not reject H_{0}. Notice that this is a onesided decision rule corresponding to our onesided research hypothesis (the twosided situation is discussed in the next example).
Table of Critical Values for the Sign Test
The critical values for the Sign Test are in the table below.
To determine the appropriate critical value we need the sample size, which is equal to the number of matched pairs (n=8) and our onesided level of significance α=0.05. For this example, the critical value is 1, and the decision rule is to reject H_{0} if the smaller of the number of positive or negative signs < 1. We do not reject H_{0} because 2 > 1. We do not have sufficient evidence at α=0.05 to show that there is improvement in repetitive behavior after taking the drug as compared to before. In essence, we could use the critical value to decide whether to reject the null hypothesis. Another alternative would be to calculate the pvalue, as described below.
Computing Pvalues for the Sign Test
With the Sign test we can readily compute a pvalue based on our observed test statistic. The test statistic for the Sign Test is the smaller of the number of positive or negative signs and it follows a binomial distribution with n = the number of subjects in the study and p=0.5 (See the module on Probability for details on the binomial distribution). In the example above, n=8 and p=0.5 (the probability of success under H_{0}).
By using the binomial distribution formula:
we can compute the probability of observing different numbers of successes during 8 trials. These are shown in the table below.
x=Number of Successes  P(x successes) 

0  0.0039 
1  0.0313 
2  0.1094 
3  0.2188 
4  0.2734 
5  0.2188 
6  0.1094 
7  0.0313 
8  0.0039 
Recall that a pvalue is the probability of observing a test statistic as or more extreme than that observed. We observed 2 negative signs. Thus, the pvalue for the test is: pvalue = P(x < 2). Using the table above,
Because the pvalue = 0.1446 exceeds the level of significance α=0.05, we do not have statistically significant evidence that there is improvement in repetitive behaviors after taking the drug as compared to before. Notice in the table of binomial probabilities above, that we would have had to observe at most 1 negative sign to declare statistical significance using a 5% level of significance. Recall the critical value for our test was 1 based on the table of critical values for the Sign Test (above).
OneSided versus TwoSided Test
In the example looking for differences in repetitive behaviors in autistic children, we used a onesided test (i.e., we hypothesize improvement after taking the drug). A two sided test can be used if we hypothesize a difference in repetitive behavior after taking the drug as compared to before. From the table of critical values for the Sign Test, we can determine a twosided critical value and again reject H_{0} if the smaller of the number of positive or negative signs is less than or equal to that twosided critical value. Alternatively, we can compute a twosided pvalue. With a twosided test, the pvalue is the probability of observing many or few positive or negative signs. If the research hypothesis is a two sided alternative (i.e., H_{1}: The median difference is not zero), then the pvalue is computed as: pvalue = 2*P(x < 2). Notice that this is equivalent to pvalue = P(x < 2) + P(x > 6), representing the situation of few or many successes. Recall in twosided tests, we reject the null hypothesis if the test statistic is extreme in either direction. Thus, in the Sign Test, a twosided pvalue is the probability of observing few or many positive or negative signs. Here we observe 2 negative signs (and thus 6 positive signs). The opposite situation would be 6 negative signs (and thus 2 positive signs as n=8). The twosided pvalue is the probability of observing a test statistic as or more extreme in either direction (i.e.,
When Difference Scores are Zero
There is a special circumstance that needs attention when implementing the Sign Test which arises when one or more participants have difference scores of zero (i.e., their paired measurements are identical). If there is just one difference score of zero, some investigators drop that observation and reduce the sample size by 1 (i.e., the sample size for the binomial distribution would be n1). This is a reasonable approach if there is just one zero. However, if there are two or more zeros, an alternative approach is preferred.
 If there is an even number of zeros, we randomly assign them positive or negative signs.
 If there is an odd number of zeros, we randomly drop one and reduce the sample size by 1, and then randomly assign the remaining observations positive or negative signs. The following example illustrates the approach.
Example:
A new chemotherapy treatment is proposed for patients with breast cancer. Investigators are concerned with patient's ability to tolerate the treatment and assess their quality of life both before and after receiving the new chemotherapy treatment. Quality of life (QOL) is measured on an ordinal scale and for analysis purposes, numbers are assigned to each response category as follows: 1=Poor, 2= Fair, 3=Good, 4= Very Good, 5 = Excellent. The data are shown below.
Patient  QOL Before Chemotherapy Treatment  QOL After Chemotherapy Treatment 

1  3  2 
2  2  3 
3  3  4 
4  2  4 
5  1  1 
6  3  4 
7  2  4 
8  3  3 
9 
Statistics Definitions > Non Parametric (Distribution Free) Data and Tests
What is a Non Parametric Test?
A non parametric test (sometimes called a distribution free test) does not assume anything about the underlying distribution (for example, that the data comes from a normal distribution). That’s compared to parametric test, which makes assumptions about a population’s parameters (for example, the mean or standard deviation); When the word “non parametric” is used in stats, it doesn’t quite mean that you know nothing about the population. It usually means that you know the population data does not have a normal distribution.
For example, one assumption for the one way ANOVA is that the data comes from a normal distribution. If your data isn’t normally distributed, you can’t run an ANOVA, but you can run the nonparametric alternative–the KruskalWallis test.
If at all possible, you should us parametric tests, as they tend to be more accurate. Parametric tests have greater statistical power, which means they are likely to find a true significant effect. Use nonparametric tests only if you have to (i.e. you know that assumptions like normality are being violated). Nonparametric tests can perform well with nonnormal continuous data if you have a sufficiently large sample size (generally 1520 items in each group).
When to use it
Non parametric tests are used when your data isn’t normal. Therefore the key is to figure out if you have normally distributed data. For example, you could look at the distribution of your data. If your data is approximately normal, then you can use parametric statistical tests.
Q. If you don’t have a graph, how do you figure out if your data is normally distributed?
A. Check the skewness and Kurtosis of the distribution using software like Excel (See: Skewness in Excel 2013 and Kurtosis in Excel 2013).
A normal distribution has no skew. Basically, it’s a centered and symmetrical in shape. Kurtosis refers to how much of the data is in the tails and the center. The skewness and kurtosis for a normal distribution is about 1.
Negative kurtosis (left) and positive kurtosis (right)
If your distribution is not normal (in other words, the skewness and kurtosis deviate a lot from 1.0), you should use a non parametric test like chisquare test. Otherwise you run the risk that your results will be meaningless.
Data Types
Does your data allow for a parametric test, or do you have to use a non parametric test like chisquare? The rule of thumb is:
A skewed distribution is one reason to run a nonparametric test.
 One or more assumptions of a parametric test have been violated.
 Your sample size is too small to run a parametric test.
 Your data has outliers that cannot be removed.
 You want to test for the median rather than the mean (you might want to do this if you have a very skewed distribution).
Types of Nonparametric Tests
When the word “parametric” is used in stats, it usually means tests like ANOVA or a t test. Those tests both assume that the population data has a normal distribution. Non parametric do not assume that the data is normally distributed. The only non parametric test you are likely to come across in elementary stats is the chisquare test. However, there are several others. For example: the Kruskal Willis test is the non parametric alternative to the One way ANOVA and the Mann Whitney is the non parametric alternative to the two sample t test.
The main nonparamteric tests are:
The following table lists the nonparametric tests and their parametric alternatives.
Advantages and Disadvantages
Compared to parametric tests, nonparametric tests have several advantages, including:
 More statistical power when assumptions for the parametric tests have been violated. When assumptions haven’t been violated, they can be almost as powerful.
 Fewer assumptions (i.e. the assumption of normality doesn’t apply).
 Small sample sizes are acceptable.
 They can be used for all data types, including nominal variables, interval variables, or data that has outliers or that has been measured imprecisely.
However, they do have their disadvantages. The most notable ones are:
 Less powerful than parametric tests if assumptions haven’t been violated.
 More laborintensive to calculate by hand (for computer calculations, this isn’t an issue).
 Critical value tables for many tests aren’t included in many computer software packages. This is compared to tables for parametric tests (like the ztable or ttable) which usually are included.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.
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