Abstract
This article provides the foundation for employing nonparametric testing in dental clinical research. To make wise judgments in their research, investigators should learn more about the main nonparametric tests and their particular uses. Biostatistical analysis is essential in dental research; dental research frequently deviates from the assumptions that underpin traditional parametric statistics. Nonparametric statistics are useful for studies with small sample sizes, nominal- or ordinal-level data, and non-normally distributed variables. These statistical tests make no assumptions about the sampled population. Nonparametric tests are statistical methods based on signs and ranks. For dental research to be conducted effectively and accurately, statistical approaches must be applied correctly. Therefore, dental researchers must understand the many statistical methods at their disposal and know when to use them.
1. Introduction
Evidence-based dentistry (EBD) is the integration of patient choices, clinical skills, and the best available information [1]. EBD is widely related to clinical decision-making in everyday dental practice. Oral clinicians find and use the most valid information, along with knowledge, experience, and judgment, to address real-world clinical problems in an effort to improve patient oral healthcare [2,3].
To determine the best evidence for a given clinical question, scientific information or evidence must be methodically gathered and analyzed. This evidence arises from well-designed studies that use different methodological designs, including systematic reviews (meta-analyses), experimental studies (randomized/controlled, cross-over, split-mouth, or non-randomized trials), and observational studies (cohorts, cross-sectional, or case–control), among others [4]. After that, the gathered information and findings must be directly applied to clinical practice. In dentistry, translational research plays a critical role in connecting the knowledge gap between reliable scientific findings and practical application (“from bench to bedside”). In order to determine the effectiveness, efficiency, and safety of novel oral interventions, this method gradually advances through experimental human research that incorporates data from in vitro and/or animal investigations [1,5].
A universal technique for evaluating a conclusion’s validity from dental clinical studies is statistical analysis. Statistical data analysis is a crucial component of a dental study [4,6]. Dental researchers can make inferences from ambiguous information and give meaning to apparently meaningless sets of numbers or data through the use of statistical techniques. As a result, the process is a creative production that gives data life [4,7]. A myriad of dental articles have described the principles of statistical analysis in dentistry in order to know the different methods for analyzing numerical data [8,9]. These data are collected from clinical, laboratory, or epidemiological studies that follow the methodological designs mentioned before.
However, the incorrect application of statistical techniques can lead to incorrect conclusions and inaccuracies, decreasing the article’s significance [10,11]. Numerous dentistry publications have been trying to recognize and minimize statistical mistakes. As a result, many articles have detected a wide range of these types of errors [12]. This fact has encouraged the editors to improve the quality of their publications by creating author and reviewer checklists or guidelines to help lower statistical mistakes [13]. One of the most common errors found in journals is the application of parametric statistical techniques to nonparametric data [7]. This is presumed to be because dental researchers have been trained mostly in parametric statistics, and many statistical software packages strongly support parametric statistical techniques [10].
Parametric tests are statistical tests that assume that the data are normally distributed and follows a symmetrical distribution. These tests typically have enough statistical power (they can detect a significant effect when one truly exists) and allow one to make inferences and predictions about a population based on sample data. So, parametric tests can obtain robust mathematical results. That is why these techniques are the most widely taught in universities. However, if the assumptions for the procedures are not met, it seems prudent, in the sense of robustness, to use nonparametric tests [10,11,14].
Nonparametric statistical tests (or “distribution-free tests”) are essential tools in dental clinical research, particularly when data do not meet the assumptions required for parametric tests [7,10,15]. These tests do not assume a specific distribution for the data and are often used when dealing with small sample sizes, ordinal data, or non-normally distributed data (severely skewed data) [6,10,13,16]. Research in the health sciences can greatly benefit from the application of nonparametric methodologies, despite the misunderstandings and little exposure to them [10,13]. Therefore, the present article aims to boost our understanding of nonparametric statistical analysis and its applications by providing actual and pertinent cases of the use of these techniques in dental research.
2. Why Nonparametric Tests?
Three main parametric assumptions—the normal distribution of the dependent variable, the sample size, and the degree of measurement—are consistently infringed by oral sciences researchers [6,11,13,16]. In this regard, in dental clinical research, data often do not follow the normal distribution due to various factors such as (i) small sample sizes, where many clinical studies involve limited participants (e.g., n < 25–30), leading to non-normal data distributions; (ii) ordinal data, where measures such as pain scales or satisfaction ratings are ordinal and not suitable for parametric tests; and (iii) outliers, where nonparametric tests are more robust against outliers that might skew parametric data distributions [10,11]. In terms of confirming the assumption of data normality, Shapiro–Wilk and Kolmogorov–Smirnov tests are sufficient to assess this property [6,11]. The main distinction between parametric and nonparametric statistical analysis is that the former employs original data values, while the latter uses just + or − signs or the rank of data sizes. Stated differently, nonparametric analysis is concerned more with the order of the data size than the actual value of the data [7,10,17]. According to Okoroiwu and Akwiwu [11], the main advantages of nonparametric tests are that (i) they require limited assumptions to be made about the format of the data when parametric techniques are not valid; (ii) it is less probable to reach incorrect conclusions because population assumptions are unnecessary; (iii) they can deal with unexpected outliers, which may be problematic with parametric methods; and (iv) they are very intuitive and do not require deep statistical knowledge. In contrast, the disadvantages of these tests are that (i) they may lack statistical power; (ii) they are oriented towards hypothesis testing rather than effect estimation; (iii) the necessary information is usually limited, so the results are more difficult to interpret; (iv) data information is not fully used; and (v) tied values may be problematic and adjustments are required.
3. Common Nonparametric Tests Employed in Dental Research
3.1. Pearson’s Chi-Square Test
The chi-square test compares proportions and tests the association between categorical variables. A random sample of data is used to assess how well the observed and expected outcomes fit together. It compares observed data frequencies to expected frequencies without correlation between variables. To ascertain whether the association is statistically significant, the test computes a chi-squared statistic, which is subsequently compared to a critical value [4,7,18,19]. A variant of the chi-square method is Fisher’s exact test, employed to ascertain whether two category variables significantly correlate with one another (2 × 2 contingency table). It is especially helpful when the sample size is small or the chi-square test’s assumptions are not met [4,7,20].
3.2. McNemar’s Test
McNemar’s test is a statistical test used for analyzing paired nominal data. The method is also applied to 2 × 2 contingency tables with dichotomous variables (e.g., “yes” or “no”, “female” or “male”, “healthy” or “sick”, etc.) of correlated or matched pairs of participants. In oral investigation, researchers often convert numerical data to dichotomous data before statistical analyses. This process, called dichotomization, makes data summarization more efficient and allows for a more simple interpretation of results. The McNemar test is the most appropriate tool for analyzing pre- and post-differences in dependent (related) samples [21].
3.3. Mann–Whitney U Test (Wilcoxon Rank Sum Test)
This test examines differences between medians, rather than means, from two separate groups where the dependent variable is either ordinal or continuous but not distributed normally [6]. It can be used in place of an unpaired t-test [15]. Two sets of raw data are combined and then scored based on an ordered classification. The data are ranked from lowest to highest, ignoring the group to which they belong. The sum of the ranks (not the original values) in each group and the respective medians are then statistically contrasted [4,7,15,17]. Nevertheless, the Mann–Whitney test lacks statistical power for relatively small samples. In fact, regardless of how much the groups differ, the Mann–Whitney test will always yield p-values greater than 0.05 if the total sample size is seven or less [22].
3.4. Wilcoxon Signed-Rank Test
The nonparametric analog of the t-test is the Wilcoxon signed-rank test, which may be used when the one-sample t-test assumptions are not met (small samples and the differences are not normally distributed). This more powerful method computes the difference in measurements between paired data or non-independent observations (e.g., before-and-after studies on the same participants or pair-sampled studies) [6]. It is a one-sample procedure for inferring the median difference in a matched pair setting, in which the relative magnitude of these differences is examined [17]. The procedure consists of converting the difference values to rank order and then computing the t statistic using the ranks (not the original observations) [4,7,17].
3.5. Kruskal–Wallis Test
The Kruskal–Wallis test is a nonparametric method that compares the medians of three or more independent groups to test whether samples originate from the same distribution [23]. It is used when the assumptions of the one-way ANOVA are not met, particularly the assumption of normality [4,24]. The method assigns ranks to the original raw values and then compares the sums of ranks to what would be expected if there were no differences among groups [7]. Post hoc median comparisons between pairs (e.g., Bonferroni’s correction, Dunn’s test, etc.) can be performed with the Mann–Whitney U test, adjusting the significance level [4,23,24].
3.6. Friedman Test
This rank test compares three or more related or matched groups, in which ranks replace the original data [11]. It is also useful when the data does not meet the assumptions required for a parametric test, such as the repeated measures ANOVA [23]. The scores of a single participant are ranked, and the total ranks are the same for each participant, automatically eliminating the differences among participants. Then, the sum of the ranks is calculated for each condition (or period), and the test statistic is computed. As with other nonparametric tests, tied values must be adjusted through averaging for rank computing [24]. The Friedman test also allows for multiple paired comparison post hoc procedures, which can be adjusted using the Bonferroni alpha level or through the Wilcoxon signed-rank test [23,25].
4. Examples of Application of Nonparametric Analysis in Dental Research
Table 1 [25,26,27,28,29,30,31,32,33,34,35,36,37,38] describes a variety of representative examples of recently published randomized controlled clinical trials (last 10 years) that applied nonparametric statistical tests in different areas of dental clinical research. In this table, several dental clinical studies are summarized, including aims, clinical field, methods (sample size, clinical procedures, outcome measurement methods, etc.), nonparametric analysis techniques, and main findings and conclusions. For this task, two experienced authors (AGR and APG) performed a PubMed search for recent articles (using the NCBI filter “Publication date: 10 years”). The following MeSH terms were used: “dental research”, “statistics, nonparametric”, and “clinical trials as topic”. These selected examples are presented intending to show the reader some applications of nonparametric statistical tests and their usefulness in dental clinical research.
Table 1.
Examples of published randomized controlled clinical trials that applied nonparametric statistical tests.
5. Some Recommendations for the Appropriate Use of Nonparametric Tests
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- Nonparametric tests are ideal for small sample sizes, but larger sample sizes increase test power.
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- Ensure proper identification of the data type (ordinal, nominal, or continuous).
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- Verify assumptions specific to each test.
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- Collect data meticulously, considering potential biases.
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- Use appropriate calibrated tools for measuring outcomes.
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- Use statistical software for accurate calculations (e.g., SPSS, R, Jamovi, Excel, etc.).
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- Interpret results in terms of clinical significance, not just statistical significance.
In summary, nonparametric tests are essential tools in dental research for analyzing ordinal and non-normal data. They provide robust and flexible methods for comparing groups and evaluating treatment outcomes, making them a crucial part of any clinical study. Understanding when and how to use these tests ensures robust and reliable results, aiding in better clinical decision-making and advancing dental research.
6. Limitations of Nonparametric Tests
It is important to consider the limits of nonparametric tests. When the underlying assumptions are met, these tests are less robust than parametric tests. Furthermore, nonparametric tests typically rely on weak assumptions about the distribution and/or population variance equality. Because they are continuous, they may not work for all data. Moreover, nonparametric tests assess the rankings rather than the original values and only consider order correlations among data, which may lead to a loss of information. Despite these drawbacks, nonparametric tests are the best option when the data do not comply with the conditions of parametric tests or when a distributional model for the data is not available [14].
7. Conclusions
For dental research to be conducted effectively and accurately, statistical approaches must be applied correctly. Therefore, it is critical that dental researchers understand the basic statistical methods at their disposal and know when to use them. Currently, nonparametric techniques are widely applicable to clinical research in dentistry. When it comes to nonparametric statistics, dental researchers need to be open to experimenting with and questioning these types of analytical techniques, since they are probably most appropriate for a significant number of current oral clinical trials.
Author Contributions
J.C.F.-A. and S.A.-R.: data collection, investigation, and writing—original draft, and writing—review and editing. R.M.-M. and V.Z.-A.: data collection and draft review and editing. A.P.-G. and A.G.-R.: conceptualization, writing—original draft, writing—review and editing, and general supervision. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
No new data were created in this study. Data sharing does not apply to this study.
Acknowledgments
The authors of this study would like to thank Ilse Garrocho-Cortés for her valuable help in writing and editing this manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
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