Nonparametric methods still use traditional statistical. Parametric and nonparametric statistics phdstudent. The friedman test is a non parametric test w hich was developed and implemented by milton friedman. The parametric test process mainly depends on assumptions related to the shape of the normal distribution in the underlying population and about the parameter forms of the assumed distribution. Gibbons1993 observed that ordinal scale data, notably likerttype scales, are very common in social sciences and argued these should be analyzed with nonparametric tests. Knowing the difference between parametric and nonparametric test will help you chose the best test for your research. Wilcoxonmannwhitney u test and wilcoxon rank sum test 2 equivalent tests wilcoxon rank sum.
Non parametric tests do not make as many assumptions about the distribution of the data as the parametric such as t test do not require data to be normal good for data with outliers nonparametric tests based on ranks of the data work well for ordinal data data that have a defined order, but for which averages may not make sense. Nonparametric tests are distributionfree and, as such, can be used for nonnormal variables. However, if one or more of the assumptions have been violated, then some but not all statisticians advocate transforming the data into a format that is compatible with the appropriate nonparametric test. A statistical test used in the case of nonmetric independent variables, is called nonparametric test. Difference between parametric and nonparametric test with. We have covered a number of testing scenarios and a parametric and nonparametric test for each of those scenarios. One sample single set of observations the sign test is used to test the null hypothesis that the median of a distribution is equal to some value. They are perhaps more easily grasped by illustration than by definition. When data are collected from more than two populations, the multiple sample analysis procedure can test for significant differences between the population medians using either a kruskalwallis test. Parametric tests and analogous nonparametric procedures as i mentioned, it is sometimes easier to list examples of each type of procedure than to define the terms.
Pdf a comparison of parametric and nonparametric statistical tests. Spss nonparametric tests are mostly used when assumptions arent met for other tests such as anova or t tests. A nonparametric statistical test is a test whose model does not specify conditions about the parameters of the population from which the sample was drawn. Because the distribution from which the sample is taken is speci. Comparative analysis of parametric and nonparametric tests. In parametric tests, data change from scores to signs or ranks.
Parametric tests are said to depend on distributional assumptions. For this reason, categorical data are often converted to. Alternative nonparametric tests of dispersion viii. The parametric test uses a mean value, while the nonparametric one uses a median value. Parametric data is data that clusters around a particular point, with fewer outliers as the distance from that point increases. Nonparametric tests nonparametric methods i many nonparametric methods convert raw values to ranks and then analyze ranks i in case of ties, midranks are used, e. Nonparametric statistics is the branch of statistics that is not based solely on parametrized families of probability distributions common examples of parameters are the mean and variance. A statistical test, in which specific assumptions are made about the population parameter is known as parametric test. Denote this number by, called the number of plus signs. Nonparametric methods apply in all other instances. Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. Howard is a clinical psychologist and a professional writer and he has been partnering with patients to create positive.
A parametric test is used on parametric data, while nonparametric data is examined with a nonparametric test. Parametric and nonparametric tests blackwell publishing. Nonparametric statistics portland state university. A comparison of parametric and nonparametric methods. If a nonparametric test is required, more data will be needed to make the same conclusion. Additional examples illustrating the use of the siegeltukey test for equal variability test 11. The nonparametric tests option of the analyze menu offers a wide range of nonparametric tests, as illustrated in figure 5. Some of the most common statistical tests and their nonparametric analogs. A parametric test is a hypothesis testing procedure based on the assumption that observed data are distributed according to some distributions of wellknown form e.
Strictly, most nonparametric tests in spss are distribution free tests. The two methods of statistics are presented simultaneously, with indication of their use in data analysis. Oddly, these two concepts are entirely different but often used interchangeably. Nonparametric methods nonparametric statistical tests. Parametric tests and analogous nonparametric procedures. There are two types of test data and consequently different types of analysis. The pdf for a test statistic is called the sampling distribution of the statistic. It is worth repeating that if data are approximately normally distributed then parametric tests as in the modules on hypothesis testing are more appropriate. Non parametric tests non parametric methods i many non parametric methods convert raw values to ranks and then analyze ranks i in case of ties, midranks are used, e. The second drawback associated with nonparametric tests is that their results are often less easy to interpret than the results of. This book comprehensively covers all the methods of parametric and nonparametric statistics such as correlation and regression, analysis of variance, test construction, onesample test to ksample tests, etc. Choosing between parametric and nonparametric tests.
As implied by the name, nonparametric statistics are not based on the parameters of the normal curve. The term parametric is intended to refer to statistical tests that make assumptions about particular population parameters e. This is often the assumption that the population data are normally distributed. A comparison of parametric and nonparametric statistical. Most nonparametric tests apply to data in an ordinal scale, and some apply to data in nominal scale. Chapter nonparametric statistics mit opencourseware. In statistical inference, or hypothesis testing, the traditional tests are called parametric tests because they depend on the speci. Assume the following test scores have been obtained. As i mentioned, it is sometimes easier to list examples of each type of procedure than to define the. The intervention was treatment with betamethasone, 12 mg intramuscularly daily for two consecutive days at 3436 weeks of pregnancy. The parametric tests will be applied when normality and homogeneity of variance assumptions are satisfied otherwise the equivalent nonparametric test will.
Nonparametric methods transportation research board. The parametric approach requires previous knowledge about the population, contrary to the nonparametric approach. In the following, a sample 7 observations will be used to illustrate how, when, and with what consequences nonparametric procedures can be used. Researchers investigated the effectiveness of corticosteroids in reducing respiratory disorders in infants born at 3436 weeks gestation. Statistical test these are intended to decide whether a hypothesis about distribution of one or more populations should be rejected or accepted. The model structure of nonparametric models is not specified a priori. Do not require measurement so strong as that required for the parametric tests. Non parametric tests do not make as many assumptions about the distribution of the data as the parametric such as t test do not require data to be normal good for data with outliers non parametric tests based on ranks of the data work well for ordinal data data that have a defined order, but for which averages may not make sense. Parametric and nonparametric tests in spine research. Friedmans test the friedmans test is the nonparametric test equivalent to the repeated measures anova, and an extension of the wilcoxon test it allows the comparison of more than two dependent groups two or more conditions. Although this difference in efficiency is typically not that much of an issue, there are instances where we do need to consider which method is more efficient. Nonparametric statistical tests if you have a continuous outcome such as bmi, blood pressure, survey score, or gene expression and you want to perform some sort of statistical test, an important consideration is whether you should use the standard parametric tests like ttests or anova vs. The onesample t test applies when the population is normally distributed with unknown mean and variance.
Here the variances must be the same for the populations. Remember that when we conduct a research project, our goal is to discover some truth about a population and the effect of an intervention on that population. For example, a psychologist might be interested in the depressant effects of certain recreational drugs. Nonparametric statistics includes nonparametric descriptive statistics, statistical models, inference, and statistical tests. Explanations social research analysis parametric vs. Participants were 320 women at 3436 weeks of pregnancy who. However, if normality assumptions meet then the parametric tests are more efficient. The mannwhitney u test can be used to determine if two samples of unpaired data have different median values. Parametric and non parametric test linkedin slideshare. Therefore, if your data violate the assumptions of a usual parametric and nonparametric statistics might better define the data, try running. Table 1 contains the names of several statistical procedures you might be familiar with and categorizes each one as parametric or nonparametric. Given the small numbers of bins involved n 4 ranks, tests of normality of distribution such as the. Mannwhitney test the mannwhitney test is used in experiments in which there are two conditions and different subjects have been used in each condition, but the assumptions of parametric tests are not tenable. This type of test is used for the comparison of three or more dependent.
Parametric and nonparametric tests for comparing two or. As the table below shows, parametric data has an underlying normal distribution which allows for more conclusions to be drawn as the shape can be mathematically described. Pdf this paper explains, through examples, the application of nonparametric methods in hypothesis testing. As discussed in chapter 5, the ttest and the varianceratio test make certain assumptions about the. Parametric and nonparametric tests are broad classifications of statistical testing procedures. Discussion of some of the more common nonparametric tests follows. Many times parametric methods are more efficient than the corresponding nonparametric methods. Nonparametric statistics is based on either being distributionfree or having a specified distribution but with the distributions parameters unspecified. However, there are situations in which assumptions for a parametric test are violated and a nonparametric test is more appropriate. Important probability density functions for test statistics are the t pdf for the t test statistic, the f pdf for the f test statistic, and the. Textbook of parametric and nonparametric statistics sage. Non parametric tests are distributionfree and, as such, can be used for nonnormal variables. A parametric equivalent is the twosample unpaired data students ttest the. A randomised placebo controlled trial was performed.58 873 158 847 1347 693 392 991 90 671 1533 204 165 1494 741 293 1227 540 1457 1482 1215 1453 486 1396 577 1187 462 529 95 359 528 1176 1340 1459 1093 121 537 1491 542 1073 49