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Gestão & Produção

Print version ISSN 0104-530XOn-line version ISSN 1806-9649

Gest. Prod. vol.23 no.3 São Carlos July/Sept. 2016  Epub June 20, 2016

http://dx.doi.org/10.1590/0104-530x357-15 

Articles

Non-parametric tests for small samples of categorized variables: a study

José Luiz Contador1 

Edson Luiz França Senne2 

1Programa de Pós-graduação em Administração das Micro e Pequenas Empresas, Faculdade Campo Limpo Paulista – FACCAMP, Rua Guatemala, 167, Jardim América, CEP 13231-230, Campo Limpo Paulista, SP, Brazil, e-mail: jluiz@feg.unesp.br

2Faculdade de Engenharia, Universidade Estadual Paulista – UNESP, Campus de Guaratinguetá, Av. Ariberto Pereira da Cunha, 333, CEP 12516-410, Guaratinguetá, SP, Brazil, e-mail: elfsenne@feg.unesp.br

Abstract

This paper presents a study on non-parametric tests to verify the similarity between two small samples of variables classified into multiple categories. The study shows that the only tests available for this situation are the chi-square and the exact tests. However, asymptotic tests, such as the chi-square, may not work well for small samples, leaving exact tests as the alternative. Nevertheless, if the number of classes increases, the implementation of these tests can become very difficult, in addition to requiring specific algorithms that may demand considerable computational effort. Therefore, as an alternative to the exact tests, a new test based on the difference between two uniform distributions is proposed. Computational assays are conducted to evaluate the performance of these three tests. Although non-parametric tests present numerous applications in various areas of knowledge, this study was motivated by the need to verify whether the business strategy adopted by a company is a determining factor for its competitiveness.

Keywords:  Non-parametric tests; Small samples; Computer simulation; Competitive strategy

1 Introduction

This work was motivated by a need to create an easily applied statistical test to aid research based on the development of the Fields and Weapons of Competition (FWC) model (Contador, 2008) to gauge (among other things) whether the business strategy adopted by a company is a determining factor of its competitiveness. In his research, the author of this model collected a small sample of companies and divided them into two groups. One group was formed by the most competitive companies and the other by the least competitive. The test is used to determine whether both groups adopt similar business strategies (null hypothesis H0).

The proposed test can be used for any problem with the following characteristics:

    a. Two different groups, I and II (for example, more competitive and less competitive companies), representing samples of larger populations, with n1 and n2 elements in each group, where n1 and n2 are small values;

    b. For each group or sample, the random variable assumes values of frequencies in each of the m classes, m>2 (see Table 1), i.e., the random variable is measured on a nominal scale or categorized with more than two categories

    Table 1 Frequencies of strategies (FC) for the groups of companies. 

    FC j fj gj
    A 1 2 4
    B 2 1 0
    C 3 3 2
    D 4 2 1
    E 5 2 2
    F 6 0 2

    c. The number of classes or categories that the random variable may assume (value of m) is moderate in relation to the n1 and n2 values

It should be noted that if the random variable could be classified into only two categories (e.g., two strategies), the problem could be easily solved by Fisher’s exact test (see Section 4), whatever the size of n1 and n2 of the samples from the two groups.

If, on the other hand, there were more than two categories for the random variable, but for each class a sufficiently large number of individuals (which would generate a problem with large samples), it would also be easy to determine the similarity between the two sets of responses using the chi-square text, which can fail when small samples are involved.

The other non-parametric tests that are available (sign test, Wilcoxon signal rank test, rank sum test, median test and t-test for paired dataset) are inadequate, as will be demonstrated through examples. Thus, for the case of small samples and more than two classes for the random variable, the problem is difficult to solve.

Therefore, the only safe alternative for addressing this type of problem is exact tests, such as the one presented in StatXact (2008), with the solution based on an extension of Fisher’s Exact Test (Fisher, 1970) proposed by Freeman & Halton (1951). However, the implementation of this test requires specific algorithms and, in some cases, requires considerable computational effort, which justifies the search for new tests for this type of problem.

In light of this, the present article presents a comparative performance study (capacity to decide H0 correctly) of the exact tests, chi-square and a new test based on the difference between two uniform distributions, proposed here. The effectiveness of these tests is compared using three indicators (risks α and β and the characteristic indicator, CI, extracted from the power curve, which will be constructed through simulation.

The studies developed here focus on attempting to solve the problem of strategy related to the FWC model. For this reason, some concepts of this model are given in the following section, as they are essential for understanding the problem in question. The aim of this article is not to discuss or introduce the FWC model. If the reader would like to know more about the model, a source of further information is provided in the references.

Numerous other problems related to biology, medicine and the social and human sciences have the characteristics described above and could be addressed using the statistical techniques used here. Some examples of problems directly related to social engineering are:

  • − Determining whether two different types of employees (machine operators and office workers, for example) in small companies (with few workers) have similar motivations in order to develop a single incentives program (or include all workers in a single program);

  • − Determining, through a small sample of companies from different sectors (e.g., manufacturing and services) whether these companies value the same characteristics in their executives to standardize human development programs;

  • − Determining whether executives (few in number) from different business units of a corporation have similar managerial capacity;

  • − Determining whether two different production processes, by analyzing few parts, create products with similar levels of quality for different characteristics (size, finishing, etc.).

The main result of the work was that effectiveness of the proposed test was similar to that of exact tests and that it performs well in situations in which the chi-square test fails (small samples and scanty, unbalanced data). Therefore, it is a real alternative to the exact test, the application of which often requires special software with restricted access.

In Section 3, there is a brief discussion on non-parametric tests and a critical analysis of their application to solve the problem in question (strategy). In Section 4, the solution adopted by the StatXact for problems with categorized variables is presented. In Section 5, the development of the proposed test is presented, based on the difference between two uniform distributions. In Section 6, the studies conducted to assess the performance of the three tests (the proposed test, the exact test and the chi-square) are presented. The conclusions are given in Section 7. This final section also shows how the proposed test can be extended for problems with more than two independent samples, and are presented two examples in which the proposed test shows a clear advantage over the chi-square.

2 Fields and weapons of competition model

According to the FWC model, companies focus their competitive strategy on one of the 14 fields of competition (clustered in five macro fields), although they can adopt another (two or three) supporting fields. The fields of competition, according to the FWC model, are as follows:

  • Macro-field of competition in price: (1) the price itself, (2) payment conditions, and (3) prize and/or promotion;

  • Macro-field of competition in product, goods or services: (4) product project, (5) product quality, and (6) variety of models;

  • Macro-field of competition in attendance: (7) presales technological service, (8) assistance during sale, and (9) after-sales technical service;

  • Macro-field of competition in delivery time: (10) deadline of budgeting and negotiation, and (11) product delivery deadline;

  • Macro-field of competition in image: (12) product and brand name, (13) reliability of the company, and (14) social responsibility (civil and preservationist).

The test of the FWC model assumes that a company’s competitiveness is not determined by its choice of competitive strategy. Rather, it is the correct alignment of its core competence (Hamel & Prahalad, 1995) with the chosen field of competition, whatever it may be. Evidently, the model assumes that it is necessary to choose for each product/market pair one of the fields that is of interest to the market.

For a better understanding of the problem in question, consider the data in Chart 1, extracted from one of the studies conducted by Contador (2008), with the set of 21 companies which, by degree of competitiveness (DC), were divided into two groups: the most and least competitive. To determine the degree of competitive of the company i ( DCi ), the FWC model normally uses the variation that occurs in a given period of time for invoicing or net turnover of the company.

Chart 1 Classification of companies in the most and least competitive groups. 

Group I: Most competitive companies Group II: Least competitive companies
Code Main field of competition (FC) DCi Code Main field of competition (FC) DCi
Denomination FC Denomination FC
E10 Product and brand image A 1.51 E05 Variety of models D 0.82
E13 Product delivery deadline B 1.43 E11 After-sales service C 0.80
E17 After-sales service C 1.39 E06 Product and brand image A 0.79
E19 After-sales service C 1.32 E12 Product and brand image A 0.79
E21 Variety of models D 1.25 E04 Product and brand image A 0.69
E02 Product and brand image A 1.19 E14 Presales service F 0.62
E08 Product project E 1.16 E16 Product project E 0.54
E03 After-sales service C 1.14 E07 Product and brand image A 0.47
E13 Product project E 1.11 E09 Presales service F 0.38
E01 Variety of models D 1.07 E20 Product project E 0.30
E18 After-sales service C 0.25

Source: Contador (2008).

A company i is classified as being in the group of the most or least competitive, in the FWC model, using the Nihans index (N). For a group of n companies, the Nihans index is calculated using the following formula (Equation 1):

N=i=1nGCi2i=1nGCi (1)

Thus, if DCi N, then the company is classified among the most competitive. Otherwise, it is considered as among the least competitive.

The FC column for each group of companies in Chart 1 represents the codes of the main fields of competition declared by the respective companies. Thus, the strategies of both groups of companies can be represented by the C1 lists (Set 1 - the most competitive companies) and the C2 (Set 2 - the least competitive companies), as follows:

C1 = {A, A, B, C, C, C, D, D, E, E} Set 1

C2 = {A, A, A, A, C, C, D, E, E, F, F} Set 2

Therefore, the null hypothesis H0 considers that the lists of strategies C1 and C2 are samples from the same population and, if it is not possible to reject H0, it is accepted that the choice of business strategy is not a determiner of the level of competitiveness of a company. The aim of the presented work is to study how to answer this question through statistical tests.

This type of test may be done by determining whether the sets of values fj and gj can be considered as coming from the same population, where fj and gj are the distributions of the frequencies with which the strategies j = 1, 2, ..., m appear in Group I and Group II of companies, respectively, so that j=1mfj=n1 and j=1mgj=n2 . For the case of Chart 1, fj and gj assume the values expressed in Table 1.

3 Non-parametric tests and the problem of similarity of strategies

Non-parametric statistics include a large number of inference techniques whose preponderant factors are the few assumptions regarding how the data were generated. Normally, they only require the samples to be independents or the data to be obtained at random.

The fundamental problem in non-parametric statistics is determining, from the data of a sample, the probability value ρ (tail value) that will lead to the decision whether to accept the null hypothesis, which can be done in two ways:

    a. Using the equation ρ=P(Xxcal), where X represents a known probability distribution and xcal is a value calculated from a (statistical) function of the sample data, so that xcal ∈ X; or

    b. Using the equation ρ= i=1rpi , where pi , for i=1, is the probability of that configuration of values occurring reflected by the sample and pi , i = 2, ..., r is the probability of any one of the other possible (r – 1) occurring, more extreme than the original sample.

Small ρ values (normally lower than α = 0.05) indicate that the null hypothesis (H0) should be rejected. Thus, it is vitally important to determine the value of ρ as accurately as possible.

The way that the ρ value is calculated divides non-parametric tests into two classes: approximate tests (or asymptotic tests), when ρ is determined as in (a), described above, and exact tests, when ρ is calculated as in (b). When the first way is chosen, for the obtained ρ value to be reliable, it is necessary to be certain that the test variable xcal reproduces, with good approximation, a distribution element of X. A requisite condition for this is that the size of the sample should be sufficiently large. For this reason, they are called asymptotic tests. On the other hand, using method (b) there is the exact value for each pi, which accounts for the origin of the term exact test.

A very common problem in statistical inference is determining, for a given level of test α, i.e., with the certainty of (1 – α), whether differences observed in two samples mean that the corresponding populations really differ from one another, which would lead to the rejection of the null hypothesis H0, coinciding with the problem of interest to the FWC model.

The first tests developed in non-parametric statistics belong to the class of asymptotic tests. Lehmann (1975) attributes to John Arbuthnot (1710) the first work in the field through the presentation of the sign test, the purpose of which was to verify whether two samples stem from the same population, applying it to problems with ordinal variables. For a discussion on the types of variables (ordinal or categorized), several works can be consulted, including that of Siegel & Castellan (2006)

Pearson (1900) made a significant advance towards the creation of non-parametric tests applied to nominal or categorized variables. He demonstrated that the statistical test based on the sum of m samples formed by the differences between the observed frequency and expected frequency of variables distributed into m categories, when generated from a multinomial, hypergeometric or Poisson distribution, have a chi-square distribution providing the sample size is sufficiently large. This resulted in one of the most important asymptotic non-parametric tests (chi-square), applicable to a wide range of problems with categorized variables.

In the mid-twentieth century, non-parametric methods applied to problems with ordinal variables were given a boost by an article by Wilcoxon (1945), which presented a test based on the sum of ranks of two samples to verify whether they were extracted from the same population. Later, Mann & Whitney (1947) developed a more adequate procedure, which resulted in the Wilcoxon-Mann-Whitney test (Mann, Whitney and Wilcoxon, and others, independently proposed non-parametric tests that are essentially identical)

Other important works on non-parametric statistics that also addressed ordinal variables are those of Friedman (1937), Pitman (1937a, b, c), Kendall (1938), Smirnov (1939), Wald & Wolfowitz (1940), Kruskal & Wallis (1952), and Chernoff & Savage (1958).

From these works the following non-parametric tests were derived, and could be applied to the problem in question: sign test; Wilcoxon’s sign rank test (1945); Wilcoxon-Mann-Whitney rank sum test; chi-square, median test and the t-test for paired dataset. However, these tests are inadequate for addressing problems with small samples and categorized variables, as shown in their application to the data in Table 2.

Table 2 Data for the application of the tests available in the literature. 

Sample j 1 2 3 4 5 6 7 8 9 10 11
A 1 fj 5 4 5 4 5 4 4 4 4 4 5
A 2 gj 2 1 2 1 2 1 5 5 4 5 5

Intuitively, it is difficult not to accept that there is no distinction between the two samples, as in six of the eleven classes there is a considerable difference between variables fj and .

In the sign test, as Respondent A surpasses B in six of the eleven requirements and is surpassed in three (there is a draw), a tail value equal to 0.254 is obtained, proving to be the true H0. The Wilcoxon test provides a tail value of ρ = 0.062, for T+ = 51 and n=11 and, through the Wilcoxon-Mann-Whitney test, for the variable of the test z=1.04 is obtained, which provides a two-tailed value equal to 0.298. When the median test is applied, for the respective contingency table, a chi-square value is obtained equal to χcal2 = 1.692, showing that there is no distinction between the respondents (tail value ρ = P[ χ12 > 1.692]=0.193). If we apply the t-test for paired dataset a two-tailed value of ρ = 0.061 is obtained. Finally, if we apply the chi-square test, we will obtain a tail value of ρ = 0.675.

Therefore, all the tests led to the conclusion that they would be the opposite of what was expected. This happened because for a statistical test to function adequately for the problem in question, the respective test variable Xcal, calculated from the data of the two samples, to be used to determine ρ =P[XXcal], must have three properties. They are: a) considering the extent of the difference observed in each pair of values related to each class of random variable; b) accumulating the differences in opposite senses observed in different classes (stop one from annulling the other); and c) being adjusted to a known probability distribution X.

The only test among those applied with the first two properties is the chi-square. However, to meet the third requirement, it is necessary for at least 80% of the cells to have a frequency greater than 5 and no cell with a frequency less than 1 (Siegel & Castellan, 2006), which does not occur in the data in Table 3.

Table 3 Data to exemplify Fisher’s exact test. 

Groups Groups Groups
I II I II I II
6 3 9 7 2 9 8 1 9
2 6 8 1 7 8 0 8 8
8 9 17 8 9 17 8 9 17
(a) (b) (c)

Source: Prepared by the authors.

The chi-square can often fail if the values contained in the cells are sparse or have strong imbalance (see example in Section 7).

As an alternative to the chi-square, when the previous conditions are not met, the exact tests emerge. Fisher’s test, proposed in 1925 (Fisher, 1970), was the first of these and is applicable to two samples of variables with two categories (tables with l = 2 lines and c = 2 columns). This test was later extended to tables with l > 2 and c > 2 by Freeman & Halton (1951). However, its application requires great computational effort, principally if the number of classes is large (Sprent & Smeeton, 2000, p. 322). In these cases, appropriate software is required, such as the StatXact (2008).

The uncertainty of using the chi-square in problems with small samples and the difficulty involved in applying exact tests led the authors to propose a new non-parametric test to address problems with small samples of categorized variables and conduct comparative studies on the performance of these three tests, i.e., the capacity to decide the null hypothesis H0 correctly.

In the following section, the theory of the exact tests, especially that of Fisher, is presented, along with the procedure adopted by the StatXact software for this class of problem, with the main object being to show the difficulties involved in solving problems with small samples whose variables assume more than two categories.

4 Exact tests based on permutation theory

To exemplify the application of Fisher’s exact test to tables with a 2x2 dimension, consider Tables 3a-c, in which Group I refers to the male sex and Group II to the female sex.

In the upper line of each of these tables are the frequencies of people with a height of 1.80m or taller. In the lower line, there are the frequencies of people who are under 1.80m tall. These data were obtained from a sample of eight men and nine women. The idea is to gauge, based on this small sample, whether men are taller than women. Consider that the hypothesis H0 establishes equality of height and the alternative hypothesis H1 establishes that men are taller than women. To apply Fisher’s exact test to this problem, the value of ρ = i=1rpi is determined, where pi is the probability of an equal or more extreme situation occurring (in the sense of Hypothesis H1) than that of Table 3a, maintaining the total fixed marginal values. Observe that the sample included six men who were taller than 1.80m and two who were shorter. As the test is unilateral, (due to the alternative hypothesis H1), there are two other more extreme situations than that of Table 3a with fixed marginal values, which are represented in Tables 3b and 3c.

The exact probability of observing a particular set of frequencies in a 2x2 table, when the marginal totals are considered fixed, is given by the hypergeometric distribution, resulting in ρ = 0.109, obtained from the sum of p(a), p(b) and p(c), given by Equations 2, 3 and 4, respectively:

p(a)=9! 8! 8! 9!17! 6! 3! 2! 6! =0.0968 (2)
p(b)=9! 8! 8! 9!17! 7! 2! 1! 7! =0.0012 (3)
p(c)=9! 8! 8! 9!17! 8! 1! 0! 8! =0.0004 (4)

In this case, as ρ > 0.05, it is not possible to reject H0 with a level of certainty of 95%.

An example will now be presented to illustrate how the exact test is applied to tables with l > 2 and c > 2.

Consider the data in Table 4 as representing the number of executives that belong to four business units of a large corporation who have been given high, average and low evaluations in an executive promotion program. Based on this small sample, is it possible to conclude that Business Unit A has the most capable executives (alternative hypothesis H1)?

Table 4 Result of the evaluation of executives. 

Evaluation Level Business Units Total
A B C D
High 5 2 2 0 9
Average 0 1 0 1 2
Low 0 2 3 4 9
Totals 5 5 5 5 20

Source: Prepared by the authors.

If the chi-square tests were applied, the constructed statistic would have (l–1)×(c–1) = 6 degrees of freedom and would supply χ2 =11.555. As P( χ62>11.555 )=0.0726, it would not be possible to reject the null hypothesis H0 with 95% certainty and affirm that Business Unit A has more capable executives.

To apply the exact test, all the possible tables are generated from the configuration of the sample data, maintaining fixed marginal values. The tables that originate values of χ2 ≥11.555 represent more extreme situations that of the original sample and thus contribute with their respective values of p to compose the value of ρ. For instance, Tables 5a and 5b are two possible arrangements obtained from Table 4. The first is χ2 =14.676, and should be considered a more extreme situation that that of the original model. Thus, its respective value of p contributes to the determination of ρ. Meanwhile, Table 5b provides =9.778, and its corresponding value of p does not contribute to the calculation of ρ.

Table 5 Two permutations of the results of the evaluation of the executives. 

5 2 2 0 9 4 3 2 0 9
0 0 0 2 2 1 0 0 1 2
0 3 3 3 9 0 2 3 4 9
5 5 5 5 20 5 5 5 5 20
(a) (b)

The generalization of the calculation of probability p of a particular set of frequencies for a table with l lines and c columns, by Freeman & Halton (1951), is made using Equation 5, where ni,o is the marginal value of the line i, no,j is the marginal value of the column j, nij is the value contained in the cell (i, j) and n is the sum of the values of all the cells:

p=i(ni,o)!j(no,j)!n!i,j(nij)! (5)

In the application of the exact test to tables of dimension l×c, all the possible tables from the originating data of the sample must be represented. It is the representation of these tables that generally requires considerable computational effort.

This type of problem can be solved by software such as the StatXact (2008). For this particular case, this software arrives at ρ = 0.0398, which, contradicting the result of the chi-square test, leads to the rejection of the null hypothesis H0 with 95% certainty.

5 Test based on the difference between two uniform distributions

In this section, a new non-parametric test is presented for the problem in question. The test statistic is given by the difference between two uniform distributions of probabilities.

Let j = 1, 2, ..., k, km be the index of the alternatives that a categorized random variable C can assume, and let P = { pj , j= 1, 2, ..., k} and Q = { qj , j= 1, 2, ..., k} be the true distributions of probabilities of this variable in two different populations P1 and P2 (e.g., more competitive and less competitive companies). Consider the functions expressed by Equations 6 and 7:

pj'=pj/[(pj+qj)/2]/j=1k{pj/[(pj+qj)/2]} (6)
qj'=qj/[(pj+qj)/2]/j=1k{qj/[(pj+qj)/2]} (7)

Then, if pj = qj , for all j = 1, 2, ..., k, it can easily be seen that pj'=qj'=1/k , for all j, i.e., if P and Q have the same distribution of probabilities, then functions pj' and qj' convert the distribution of strategies j for both populations of companies into a uniform distribution with probability equal to 1/k for all j. This shows that the proposed test, which is in essence based on determining the difference for |pj'qj'| , is convergent.

Now let fj and gj , j = 1, 2, ..., m, be the frequencies that the random variable C assumes in two samples A1 and A2 of sizes n1 and n2 extracted from populations P1 and P2, respectively. As fj/n1 and gj/n2 are fair estimates for pj and qj , respectively, if A1 and A2 are samples from the same population, then the Equations 8 and 9 must have values close to 1/m, for all j = 1, 2, ..., m, for any values of n1 and n2. This fact motivated the proposition of this test for the case of small samples, despite being an asymptotic test.

rj={(fj/n1)/[(fj+gj)/(n1+n2)]}/j=1m{(fj/n1)/[(fj+gj)/(n1+n2)]} (8)
sj={(gj/n2)/[(fj+gj)/(n1+n2)]}/j=1m{(gj/n2)/[(fj+gj)/(n1+n2)]} (9)

Now, consider the statistic D = j=1m | ujvj| , where uj and vj are relative frequencies of the variable j = 1, 2, ..., m, so that Pr(j) = 1/m, for all j. This variable is only a little sensitive to the variation of the number of elements in the sample (at least for small variations, which always occurs when dealing with small samples, as is the case in question). However, it depends on the value of m, as it stems from the sum of m samples, each one given by the difference between two uniform variables. The distribution of probabilities of this statistic is not known. Nevertheless, it is possible, through simulation, to construct its histogram for diverse values of m and, from each of these histograms, determine Dα, where Dα is the value of D that leaves α% of the data to its right.

With the aid of this information, it is possible to determine whether the lists of strategies A1 and A2 stem from the same population (Hypothesis H0). It is sufficient to calculate the statistic Dcal = j=1m| rjsj| from the values of fj and gj originating from A1 and A2, respectively, and compare with the value of Dα. If Dcal > Dα, Hypothesis H0 can be rejected with a level of certainty (1-α).

Observe that the variable Dcal (like D) is defined in the interval [0, 2]. When fj = gj , for all j = 1, 2, ..., m, then Dcal = 0, which provides the maximum certainty that both sets A1 and A2 originate from the same population. Now, when, for each j = 1, 2, ..., m, ( fj = 0, gj >0) or ( fj > 0, gj =0), which means that each group of companies declared different sets of strategies and therefore the intersection of sets A1 and A2 is empty, then Dcal = 2, which provides the maximum certainty of rejection for the null hypothesis H0.

5.1 Determining the value of Dα

The value of Dα was determined from the histogram of the variable D, constructed through a computer simulation process. This procedure is illustrated below for the case of m = 6, n1 = n2 = 12.

Step 1. Establish the following correlation according Table 6, where RN is a uniform random number in the interval [0, 1].

Table 6 Correlation between uniform random number and the classes of variables. 

RN in the interval Variable C
[0, 1/6) A
[1/6, 2/6) B
[2/6, 3/6) C
[3/6, 4/6) D
[4/6, 5/6) E
[5/6, 1] F

Step 2. Generate n1 uniform random numbers (RN) in the interval [0, 1] for the first sample and other n2 numbers for the second sample, and obtain sets A1 and A2, i.e., values of fj and gj . For n1 = n2 = 12, a possible result is shown in columns fj and gj of Table 7, where, among the twelve values randomly selected for sample A1, two fell in the interval [0, 1/6), and for sample A2, three values fell in the same interval, thus originating f1 = 2 and =3.

Table 7 Application of the test of the difference of two uniform distributions. 

Strategy (j) fj gj uj vj | ulvj|
A 2 3 0.167 0.250 0.083
B 1 0 0.083 0.000 0.083
C 3 3 0.250 0.250 0.000
D 2 1 0.167 0.083 0.083
E 2 2 0.167 0.167 0.000
F 2 3 0.167 0.250 0.083
Sum 12 12 1.000 1.000 0.333

Source: Prepared by the authors.

Step 3. Determine, for each generated sample A1 and A2, D = j=1m | ujvj| , where uj=(fj/n1) , vj=(gj/n2) , as shown in Table 7, which arrives at D = 0.333 for this example.

Step 4. Repeat Steps 1 to 3 10000 times, generating 10000 ordered values for D, and identify the value of Dα, for significance levels α = 0.01 and α = 0.05 (D0.05 is given by the value of D, which leaves 500 values to its right, and D0.01 is given by the value of D that leaves 100 values to its right). Table 8 shows the critical values of Dα, for different values of m and α = 0.05 and α = 0.01.

Table 8 Critical values of Dα. 

m
α 3 4 5 6 7 8
0.05 1.143 1.250 1.200 1.167 1.143 1.125
0.01 1.429 1.500 1.400 1.333 1.286 1.250

Source: Prepared by the authors.

Applying the test to the data in Table 2, Dcal = 0.493 is obtained. As in this example m = 6, it can be concluded that the null hypothesis H0 cannot be rejected and it must be accepted that the two groups of companies adopt similar sets of strategy.

6 Study of the power of the tests

The effectiveness of the exact tests, the chi-square and the proposed test, was evaluated by analyzing the power curve, supplying the probability of the acceptance (Pa) of the null hypothesis (H0) due to the level of similarity of the two samples.

The power curve was raised using computer simulation for the level of similarity between the samples, defined by the parameter referred to as the degree of symmetry (DS) of the distributions of samples A1 and A2, varying in the interval [0, 1] and given by Equation 10, where pj and qj are the probabilities of the categorized variable originating from samples A1 and A2 for all j= {1, 2, ..., m}.

GS=(j=1m|pjqj| )/2 (10)

Defining appropriate values for pj and qj , through simulations, samples from populations with the following degrees of symmetry were obtained DS={0.0; 0.2; 0.4; 0.6 and 0.8}. Observe that if pj = qj for all j, Equation 2 provides DS = 0, and the samples obtained by simulation for this case will be from the same population. On the other hand, if pj = 0 when qj ≠ 0, for all j, then DS = 1, creating configurations with samples from totally different populations.

Computer tests were conducted for the following six configurations of problems identified by the sets of values of (m, n1, n2): (3, 7, 7), (4, 8, 8), (5, 10, 10), (6, 12, 12), (7, 14, 14) and (8, 16, 16). For each of these six cases and for each of the five values of DS mentioned above, the probability of acceptance Pa was determined according to the exact test, the chi-square and the proposed test.

For this purpose, 100 problems were generated for each of the six sets of values (m, n1, n2) and the five degrees of symmetry (DS). The value of Pa for a determined test and for a given set of values (m, n1, n2) and a given value of DS could then be identified by directly counting the number of problems in which there would be acceptance of the H0.

For all the tests, a significance level α=0.05 was adopted. Thus, the acceptance of H0 occurred whenever ρ= P[X> Xcal]> α, where X is the test variable and Xcal is the value of the statistic of the test, or whenever Xcal< Xcrit, where Xcrit is such that P[X> Xcrit]= α, which is the same thing viewed in two ways.

In all, 3000 problems were tested, 100 for each combination [(m, n1, n2); GS], and each was solved using the three tests.

The configuration of each problem, i.e., values of fj and gj for both samples was obtained as described in Steps 1 and 2 of the procedure to determine Dα, presented in Section 5.

From this curve, raised by computer simulation, the following indicators could be extracted for a comparative analysis of the tests:

    a. Risk α, which is the probability of committing a Type I error (rejecting the null hypothesis when it is true), given by α= (1-Pa), for DS=0;

    b. Average of risks β given by the average of Pa for the four values of DS>0, where β is the probability of committing a Type II error (accepting a false null hypothesis); and

    c. Characteristic indicator of the power curve (CI), determined by the relationship (Slope)0.50/(DS)0.50, where (Slope)0.50 is the slope of the curve at point (DS)0.50, with (DS)0.50 being the value of DS that originates a probability of acceptance of 50%.

The value of (Slope)0.50 was determined by Equation 11.

(Slope)0.50=(DS)0.6(DS)0.4100.(0.60.4) (11)

As it is a downward curve, the negative sign is introduced to make the result of the slope positive. The denominator was multiplied by 100 to represent it on a more adequate scale (interval [1 to 10]). The values of (DS)0.40 and (DS)0.60 were obtained by visual inspection of the graph of the power curve generated by the five points (GS, Pa).

The two parameters (Slope)0.50 and (DS)0.50 are frequently used to evaluate the discriminant power of quality inspection plans. The higher the value of (Slope)0.50 and the lower the value of (DS)0.50, the greater the power of the plan, or the power of the statistical test, in the present study. Thus, the CI expresses in a single indicator the properties of both (the higher their value, the greater the power of the test) and can dispel doubts that may remain from the application of the α and β risk indicators.

Studies on the performance of statistical tests adopts only the risk indicators, a point in question being the case of Tanizaki (1997). Thus, the use of a new indicator (CI) with the property outlined above makes a contribution to this type of study.

Tables 9a to 9f show the results obtained from the trials with the exact tests (solution obtained by the StatXact), chi-square (Chi-Squ) and presented test (Uniform). The values of Pa are expressed in percentage form, as they correspond directly to the number of problems in which there was acceptance of H0, out of 100 problems tested for each value of DS. The meaning and form of obtaining the values of CI, α, and β Average, shown in in Tables 9a, 9b, 9c, 9d, 9e, 9f will be explained in the following section.

Table 9a Results for m = 3, n1 = n2 = 7. 

Teste Probability of acceptance (Pa) - Percentage CI and Risks (%)
DS=0 DS=0.2 DS=0.4 DS=0.6 DS=0.8 CI α β Average
Exact 98 94 80 24 0 5.6 2 50
Chi-Squ 95 89 73 16 0 5.9 5 45
Uniform 97 92 77 20 0 6.6 3 47

Source: Prepared by the authors.

Table 9f Results for m =8, n1 = n2 = 16. 

Teste Probability of acceptance (Pa) - Percentage CI and Risks (%)
DS=0 DS=0.2 DS=0.4 DS=0.6 DS=0.8 CI α β Average
Exact 95 88 68 21 1 4.9 5 45
Chi-Squ 97 94 79 26 2 5.8 3 50
Uniform 91 91 72 32 5 3.9 9 50

Source: Prepared by the authors.

Table 9b Results for m = 4, n1 = n2 = 8. 

Teste Probability of acceptance (Pa) - Percentage CI and Risks (%)
DS=0 DS=0.2 DS=0.4 DS=0.6 DS=0.8 CI α β Average
Exact 97 94 78 51 12 2.3 3 59
Chi-Squ 96 93 78 50 11 2.3 4 58
Uniform 96 93 82 58 17 1.3 4 63

Source: Prepared by the authors.

Table 9c Results for m = 5, n1 = n2 = 10. 

Teste Probability of acceptance (Pa) - Percentage CI and Risks (%)
DS=0 DS=0.2 DS=0.4 DS=0.6 DS=0.8 CI α β Average
Exact 96 92 78 34 3 4.2 4 52
Chi-Squ 97 94 83 38 5 4.1 3 55
Uniform 95 92 84 39 4 3.4 5 55

Source: Prepared by the authors.

Table 9d Results for m = 6, n1 = n2 = 12. 

Teste Probability of acceptance (Pa) - Percentage CI and Risks (%)
DS=0 DS=0.2 DS=0.4 DS=0.6 DS=0.8 CI α β Average
Exact 90 94 71 28 6 4.3 10 50
Chi-Squ 92 96 77 38 7 3.5 8 55
Uniform 91 85 74 39 9 3.9 9 52

Source: Prepared by the authors

Table 9e Results for m = 7, n1 = n2 = 14. 

Teste Probability of acceptance (Pa) - Percentage CI and Risks (%)
DS=0 DS=0.2 DS=0.4 DS=0.6 DS=0.8 CI α β Average
Exact 90 94 67 32 3 3.5 10 49
Chi-Squ 95 95 73 42 3 2.7 5 53
Uniform 94 96 70 46 3 2.3 6 54

Source: Prepared by the authors.

7 Analysis of the results and conclusions

The effectiveness of the tests was evaluated by risks α and β and the characteristic indicator of the power curve (CI).

Risk α for each configuration of problem (m, n1, n2) is given in percentage form in the respective Table 9 by the value (100- Pa) for the column DS=0, as the value of Pa corresponds, among the 100 trials conducted, to the number in which the test led to the right decision, i.e., accepting the H0 when it is true. Meanwhile, risk β, also in percentages, is given by the average of the values of Pa for all DS={0.2, 0.4 0.6, 0.8}, i.e., the probability of accepting H0 when it is not true (sample with a degree of symmetry other than zero).

The values of (Slope)0.50, for each configuration (m, n1, n2), were calculated using Equation 3. These three analysis parameters are shown in Table 10.

Table 10 Summary of the parameters of evaluation of the effectiveness of the tests. 

Parameter m Test
Exact Chi-square Uniform
Risk α (%) 3 2.0 5.0 3.0
4 3.0 4.0 4.0
5 4.0 3.0 5.0
6 10.0 8.0 9.0
7 10.0 5.0 6.0
8 5.0 3.0 9.0
Average value 5.7 4.7 6.0
Risk β average (%) 3 49.5 44.5 47.3
4 58.8 58.0 62.5
5 51.8 55.0 54.8
6 49.8 54.5 51.8
7 49.0 53.3 53.8
8 44.5 50.3 50.0
Average value 50.5 52.6 53.3
CI 3 5.9 6.6 5.6
4 2.3 1.3 2.3
5 4.1 3.4 4.2
6 3.5 2.9 4.3
7 2.7 2.3 3.5
8 5.8 3.9 4.9
Average value 3.9 3.1 4.1

Source: Prepared by the authors.

Analyzing risks α and β in Table 10, the chi-square tests has the lowest risk α of the three and risk β between the other two. However, regarding the CI indicator, it had the worst performance of the three.

The proposed test shows risks α and β, and the characteristic indicator (IC), similar to those of the exact test. This shows that both have a very similar performance.

Table 11 shows the number of problems that each test decided on correctly for the 3000 trials that took place. The exact test made the most right decisions (1753 times) while the proposed test had a slightly inferior performance to the other two.

Table 11 Number of problems with the right decision. 

DS Testes
Exact Chi-Squ Uniform
0.00 566 572 564
0.20 44 39 51
0.40 158 137 141
0.60 410 390 366
0.80 575 572 562
All 1753 1710 1684

Source: Prepared by the authors.

This analysis allows us to conclude that the exact and proposed tests had very similar performances, and that the chi-square surpasses both, at least as an instrument for decision, when the null hypothesis is true. To a certain extent, this is an unexpected conclusion, when it comes to problems with small samples. In view of this, would the chi-square a valid alternative to the exact test?

Considering the example of the data in Table 12, this is not always the case. Applying the exact test to the data in this table, by the StatXact, ρ = 0.0013 is obtained, showing that the three samples do not belong to the same population. In turn, the chi-square gives a value of ρ= 0.1342, clearly showing that for small samples with a strong imbalance, as in this example, this test does not work well. Table 5 provides another example of this phenomenon. Thus, its generalized use leads to unreliable decisions, explaining the need to seek alternative tests.

Table 12 Example of a problem with three samples. 

Sample Values
A 0 7 0 0 0 0 0 1 1
B 1 1 1 1 1 1 1 0 0
C 0 8 0 0 0 0 0 0 0

Source: StatXact (2003).

How does the proposed test behave with this type of sample?

To answer this question, it is initially necessary to observe that although the proposed test is intended for problems with two samples, it is also possible to solve problems with more samples. All that is required is to apply it to the different combinations of samples two by two. Applying the uniform test to the data in Table 12 considering two samples at a time (observe that it is necessary to eliminate the columns that contain zeros in both samples), values are obtained for Dcal equal to 1.959, 1.622 and 1.964 for the combinations A/B, A/C and B/C of samples, respectively. As the maximum value of Dcal is 2.0, the test indicates with a high degree of certainty that sample B is from a different population from the others, which the chi-square failed to identify.

If we now apply the proposed test to the data in Table 5, the values obtained for Dcal are equal to 1.750, 1.556 and 2.000 for samples A/B, A/C and A/D, respectively. As Dα=0.01 =1.429, for m=3 (case of Table 4), it can be concluded, with a high degree of certainty, that Business Unit A has the most capable executives.

These two examples show that the best alternative to the exact test, which is very difficult to apply, is the proposed test rather than the chi-square. The latter, despite having shown a good performance in the set of tests, can fail in accordance with the instance of the problem.

Financial support: The first author acknowledges financial support from CNPq (DT 307363/2015-5). The second author acknowledges financial support from CNPq (grant 303339/2013-6).

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Received: October 02, 2014; Accepted: December 18, 2015

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