# 3. Nonparametric methods

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1 3. Nonparametric methods If the probability distributions of the statistical variables are unknown or are not as required (e.g. normality assumption violated), then we may still apply nonparametric tests in order to test whether two or more groups follow the same distribution. Such methods do not assume the data to follow any predefined distribution and are therefore also called distribution-free methods. In particular, they do not deal with any particular parameters of those distributions, hence the name nonparametric methods. Nonparametric methods require nothing more than the variables to be measured at ordinal scale at least and they use only information from the ranks. This makes them on one hand more robust but on the other hand less efficient than their parametric counterparts, when the assumptions of the parametric tests are met. 83

2 3.1. The Sign Test The sign test is used as a substitude for the paired-data t-test in the following situations: if the measurement scale is only ordinal, if the normality assumption is violated, as a quick substitude for the t-test. It may also be used as a substitude for the single-sample t-test for location when the normality assumption is violated. As a test for comparing two populations, the sign test is stated in terms of the probability that values of one population are greater than values of a second population that are paired two the first (e.g. responses X and Y of same consumers to two advertisements). The null hypothesis of the two-sided test is that p = P (X > Y ) = 0.5. The alternative hypothesis is that p 0.5. (One sided alternatives would be p > 0.5 or p < 0.5.) 84

3 With n observation pairs (x i, y i ), the test statistic is T = number of plus signs, where x i > y i (+) and x i < y i ( ) and pairs with x i = y i (ties) are discarded. Under the null hypothesis: T Bin(n, 1 2 ) with E(T ) = n 2 and Var (T ) = n 4. A large sample statistic is therefore: Z = T E(T ) Var (T ) = 2T n n N(0, 1) under H 0. Example: (Azcel, Example 14.1) A management consultant wants to test if the same people rate their respective CEO differently on a scale from 1 (worst) to 5 (best) before and after receiving their MBA degree. 85

4 Describe => Distribution Analysis Task Roles => Analysis variables: change Tables => Tests for location Tests for Location: Mu0=0 Test Statistic p Value Student's t t Pr > t Sign M 4.5 Pr >= M Signed Rank S 40.5 Pr >= S

5 Example: (continued) In terms of plus and minus signs, the data is after removing the ties for CEO s 2 and 5: , such that: T = number of pluses = 12. Now for T Bin(15,0.5): 3 ( 15 ) P (T 12) = P (T 3) = k k=0 ( ) = , yielding a two-sided p-value of There is thus evidence at 5% significance level that the attitude of employees towards their CEO changes after receiving an MBA. Since the rejection happened at the right tail, the attitude change is positive. 2T n n, Applying the large sample approximation Z = we would have got applying a continuity correction: ( P (T 12)=P (T 3) P Z )=Φ( 2.065) 15 = 1 Φ(2.065) = = , yielding a two-sided p-value of

6 The sign test can also be viewed as a test of the hypothesis that the median difference between two populations is zero. To see this, note that H 0 : P (X > Y ) = 0.5 P (X Y > 0) = 0.5, which is just the defining property of Median (Y X) = 0. As such, the test my be adapted for testing whether the median of a single population is equal to any prespecified number a. The null and alternative hypotheses of the two-sided test are: H 0 : Population median = a, H 1 : Population median a. To conduct the test, we pair our data observations with a and perform the sign test. If the null hypothesis is true, then about onehalf of the signs will be pluses and one-half minuses because, by definition of the median, one-half of the population values are above it and one-half below it. 88

7 Example: (Azcel) Suppose we wish to test the null hypothesis that the median income in a certain region is \$ per family per year. The randomly sampled data is (in thousands of dollars): 22,30,28,22,34,19,42,18,16,26 30,25,29,20,17,33,32,24,15,31 or in terms of + and signs (wrt. 24): , such that T = 11 and n = 19 (disregarding the single tie). Now for T Bin(19,0.5): P (T 11) = P (T 10) P (T = 10) = 0.5 ( 19) , 10 such that the two-sided p-value is We can thus not reject the null hypothesis that the median equals

8 Describe => Distribution Analysis Task Roles => Analysis variables: difference Tables => Tests for location Tests for Location: Mu0=0 Test Statistic p Value Student's t t Pr > t Sign M 1.5 Pr >= M Signed Rank S 22.5 Pr >= S

9 3.2. The Wilcoxon Signed-Rank Test The Wilcoxon signed-rank test is designed for the same purpose and may be applied in the same situations as the sign test, that is, for testing for (differences in) location in single populations or matched pairs, when the conditions for the corresponding t-tests are not met. The Wilcoxon signed-rank test is more efficient than the sign test as it uses the full information of the ranks rather than just their signs and is thus to be preferred whenever statistical software allows its use. Its only disadvantage is that its test statistic is somewhat harder to calculate by hand and that it is slightly less robust to outliers than the sign test. As a paired-observations two-sample test, the hypotheses of the two-sided test are H 0 : median of pop. 1 = median of pop. 2, H 1 : median of pop. 1 median of pop

10 In order to perform the test, we list the pairs of observations (x 1, x 2 ) of the two populations and compute their difference D =x 1 x 2. Then we rank the absolute values of the differences D discarding any ties with D = 0. In the next step, we form separate sums of the ranks of the positive and the negative differences. The Wilcoxon T statistic is defined as the smaller of the two sums of ranks, T = min [ (+), ( ) ], where (+/ ) is the sum of the ranks of the positive/negative differences. Critical points of the test statistic T are tabulated. We reject the null hypothesis of the two-sided test if the computed value of the statistic is less than the α 2 critical point from the table. We reject H 0 against the onesided alternative H 1 : median of pop. 1 > (<) median of pop. 2 if ( ) ( (+)) is less than the α critical point from the table. 92

11 Example: (Satisfaction with CEO continued.) Satisfaction Satisfaction Rank Rank Rank CEO before MBA after MBA D D D pos.d neg.d Sum: After discarding the ties for CEO s 2 and 5, the effective sample size reduces to n = 15. Since the smaller sum is the one associated with the positive ranks, we define T = (+) = Looking up in a table we find that this is just below the 2% critical value of 20 for a two-sided test, which is at the same time the 1% critical value against the one sided alternative median satisfaction before MBA < median satisfaction after MBA. Note that we were not able to reject identical medians with such high significance with the sign test discussed earlier. 93

12 3.3. Matched versus Independent Samples Recall that both the sign test and Wilcoxon s signed rank test are tests for locations in matched samples, that is, the statistical units in both samples are assumed to be identical except for a certain characteristic, the impact of which we wish to test (often same objects in some before/after situation). The corresponding test for normally distributed observations or large samples (n 30) is Student s t-test for matched samples. All these tests are available in SAS EG by checking Tests for location in the Tables pane in Describe/Distribution Analysis applied to the differences between the observations in both samples. The median test, on the contrary, tests for identical medians in independent samples and has already been discussed in a subsection of chapter 2 about the χ 2 -tests for equality of proportions. We proceed with a more efficient test in independent samples, the Kruskal- Wallis test (2 samples: Mann-Whitney test). 94

13 3.4. The Kruskal-Wallis (W. rank sum) Test Consider drawing k independent random samples, one for each out of k populations with observations on ordinal scale at least. The Kruskal-Wallis hypothesis test (also known as Wilcoxon rank sum test) is: H 0 : All k populations have the same distribution. H 1 : Not all k populations have the same distribution. Although the test is stated in terms of distributions, it is most sensitive to differences in locations. Therefore, the procedure is actually used to test the ANOVA hypothesis of equality of k population means when the presumptions of ANOVA (normally distributed observations with equal variances in all populations) are not met. 95

14 In order to perform the test, we rank all data points in the entire set of all k populations from the smallest to the largest, without regard to which sample they come from. Then we sum all the ranks from each separate sample. Let n i, i = 1,..., k denote the sample size from population i and let n = n n k. Defining R i, i = 1,..., k as the sum of ranks from sample k, the Kruskal- Wallis test statistic is defined as: H = 12 n(n + 1) k i=1 R 2 i n i 3(n + 1). As long as each n i 5, H is approximately chi-square distributed with k 1 degrees of freedom under the null of equal distributions. Different distributions will lead to high values of H, so we reject the null hypothesis if the computed value of H exceeds χ α (k 1) for a given significance level α. 96

15 Example: Three different trucks are tested for fuel efficiency as measured in miles per gallon (mpg). Truck 1 Truck 2 Truck 3 MPG Rank MPG Rank MPG Rank Sum: 21 Sum: 62 Sum: 37 k H = 12 n(n + 1) i=1 = 12 ( = R 2 i n i 3(n + 1) ) 3 16 The p-value of the test may be calculated in Excel as CHIDIST(8.54;2)=0.014, so the fuel efficiency of the different trucks is not identical. 97

16 Nonparametric One-Way ANOVA Truck Wilcoxon Scores (Rank Sums) for Variable MPG Classified by Variable Truck N Sum of Scores Expected Under H0 Std Dev Under H0 Mean Score Kruskal-Wallis Test Chi-Square DF 2 Pr > Chi-Square Nonparametric One-Way ANOVA Truck Median Scores (Number of Points Above Median) for Variable MPG Classified by Variable Truck N Sum of Scores Expected Under H0 Std Dev Under H0 Mean Score Median One-Way Analysis Chi-Square DF 2 Pr > Chi-Square

17 Remarks: The less efficient median test was not applicable in this case because all expected frequencies were less than 5. Both the Kruskal-Wallis and the less efficient median test are available in SAS under Analyze/ ANOVA/ Nonparametric One-Way ANOVA under Wilcoxon and Median, respectively. You may ask SAS for exact p-values, which work also in small samples, but become computational intensive in large samples. In the case of only 2 samples, the teststatistic for the Kruskal-Wallis test may be written in alternative ways, known as the Mann-Whitney U test or (Wilcoxon) rank sum test. But the test results (pvalues) remain unchanged. 99

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