When do i use a nonparametric test

For example, if researchers were interested in temperature, they could examine a histogram that displays the frequencies of each temperature occurring in their sample data. It should be noted that checking normality of data produced by smaller samples can be difficult. Sometimes with a small sample, the data displayed in a histogram will be obviously asymmetrical, but there are certainly occasions in which it is impossible to tell. This is because with a small sample, the histogram may not be smooth even if the data are normal.

There might not be any significant evidence of symmetry or asymmetry, which can make it difficult to determine whether the data are normal or not. However, one way to get around this obstacle is to leverage instances in which the same measurements have been measured from a previous, larger sample in an earlier study. If your data is not normal, there are a few steps you can take prior to performing a nonparametric test. If your data has a generally skewed distribution, you could consider a transformation of the data.

When data is significantly skewed in one direction or the other, sometimes there are patterns that can be observed. 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,, or , copies and others are measured as "not detected. Hypothesis Testing with Nonparametric Tests.

In nonparametric tests, the hypotheses are not about population parameters e. Instead, the null hypothesis is more general. 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.

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.

As described here, nonparametric tests can also be relatively simple to conduct. All Rights Reserved. Date last modified: May 4, Wayne W. Nonparametric Tests. Bland MJ: The tyranny of power: is there a better way to calculate sample size?. Article PubMed Google Scholar. Skovlund E, Fenstad GU: Should we always choose a nonparametric test when comparing two apparently nonnormal distributions?.

J Clin Epidemiol. Fagerland MW, Sandvik L: Performance of five two-sample location tests for skewed distributions with unequal variances. Contemp Clin Trials. Google Scholar. Hart A: Mann-Whitney test is not just a test of medians: differences in spread can be important. Stat Med. Article Google Scholar. PubMed Google Scholar. Download references. The author thanks the editor and the reviewers for their thoughtful and constructive comments and suggestions.

You can also search for this author in PubMed Google Scholar. Correspondence to Morten W Fagerland. This article is published under license to BioMed Central Ltd. Reprints and Permissions. Fagerland, M. Download citation. Received : 11 January Accepted : 14 June Published : 14 June Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Research article Open Access Published: 14 June t-tests, non-parametric tests, and large studies—a paradox of statistical practice? Abstract Background During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests.

Methods A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney WMW test and the two-sample t-test for increasing sample size. Results The WMW test produces, on average, smaller p -values than the t-test. Conclusions Non-parametric tests are most useful for small studies. Methods Suppose that we want to compare the means or medians of a continuous variable in two independent groups. Results Case study Consider Figure 1 , which is a plot of the probability density functions of two gamma left panel and two lognormal right panel distributions.

Figure 1. Full size image. Figure 2. Figure 3. Discussion The concurrent increases—since the Seventies—in sample size and use of non-parametric tests over t-tests have a paradoxical quality.

Conclusions The use of non-parametric tests in high-impact medical journals has increased at the expense of t-tests, while the sample size of research studies has increased manyfold. References 1. To learn more about these studies, read our Technical Papers. Reason 2: Parametric tests can perform well when the spread of each group is different.

For nonparametric tests that compare groups, a common assumption is that the data for all groups must have the same spread dispersion. If your groups have a different spread, the nonparametric tests might not provide valid results. Parametric tests usually have more statistical power than nonparametric tests. Thus, you are more likely to detect a significant effect when one truly exists.

For these two distributions, a random sample of from each distribution produces means that are significantly different, but medians that are not significantly different. When you have a really small sample, you might not even be able to ascertain the distribution of your data because the distribution tests will lack sufficient power to provide meaningful results.

Typical parametric tests can only assess continuous data and the results can be significantly affected by outliers. Conversely, some nonparametric tests can handle ordinal data, ranked data, and not be seriously affected by outliers.