![]() ![]() If you collect many data points (over a hundred or so), you can look at the distribution of data and it will be fairly obvious whether the distribution is approximately bell shaped.It is not always easy to decide whether a sample comes from a Gaussian population. There are often biological or chemical reasons (as well as statistical ones) for performing a particular transform.ĬHOOSING BETWEEN PARAMETRIC AND NONPARAMETRIC TESTS: THE HARD CASES For example, you might take the logarithm or reciprocal of all values. If the data are not sampled from a Gaussian distribution, consider whether you can transformed the values to make the distribution become Gaussian. The data ire measurements, and you are sure that the population is not distributed in a Gaussian manner.Since the nonparametric test only knows about the relative ranks of the values, it won't matter that you didn't know all the values exactly. Assign values too low to measure an arbitrary very low value and assign values too high to measure an arbitrary very high value. Using a nonparametric test with these data is simple. Even if the population is Gaussian, it is impossible to analyze such data with a parametric test since you don't know all of the values. Some values are "off the scale," that is, too high or too low to measure.Examples include class ranking of students, the Apgar score for the health of newborn babies (measured on a scale of 0 to IO and where all scores are integers), the visual analogue score for pain (measured on a continuous scale where 0 is no pain and 10 is unbearable pain), and the star scale commonly used by movie and restaurant critics (* is OK, ***** is fantastic). The outcome is a rank or a score and the population is clearly not Gaussian.You should definitely select a nonparametric test in three situations: You should definitely choose a parametric test if you are sure that your data are sampled from a population that follows a Gaussian distribution (at least approximately). These tests are also called distribution-free tests.ĬHOOSING BETWEEN PARAMETRIC AND NONPARAMETRIC TESTS: THE EASY CASESĬhoosing between parametric and nonparametric tests is sometimes easy. These tests are listed in the second column of the table and include the Wilcoxon, Mann-Whitney test, and Kruskal-Wallis tests. All commonly used nonparametric tests rank the outcome variable from low to high and then analyze the ranks. You've already learned a bit about nonparametric tests in previous chapters. Tests that do not make assumptions about the population distribution are referred to as nonparametric- tests. Commonly used parametric tests are listed in the first column of the table and include the t test and analysis of variance. ![]() These tests are referred to as parametric tests. Many -statistical test are based upon the assumption that the data are sampled from a Gaussian distribution. Predict value from several measured or binomial variablesĬhoosing the right test to compare measurements is a bit tricky, as you must choose between two families of tests: parametric and nonparametric. Predict value from another measured variable Quantify association between two variables Rank, Score, or Measurement (from Non- Gaussian Population)Ĭompare one group to a hypothetical valueĬonditional proportional hazards regression*Ĭonditional proportional hazards regression** To select the right test, ask yourself two questions: What kind of data have you collected? What is your goal? Then refer to Table 37.1. This book has discussed many different statistical tests. ![]()
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