Parametric and Nonparametric: Demystifying the Terms

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2 of the parameters for that population. We can, however, calculate estimates of these quantities for our sample. When they are calculated from sample data, these quantities are called statistics. A statistic estimates a parameter. Parametric statistical procedures rely on assumptions about the shape of the distribution (i.e., assume a normal distribution) in the underlying population and about the form or parameters (i.e., means and standard deviations) of the assumed distribution. Nonparametric statistical procedures rely on no or few assumptions about the shape or parameters of the population distribution from which the sample was drawn. Parametric s and analogous nonparametric procedures As I mentioned, it is sometimes easier to list examples of each type of procedure than to define the terms. Table 1 contains the names of several statistical procedures you might be familiar with and categorizes each one as parametric or nonparametric. All of the parametric procedures listed in Table 1 rely on an assumption of approximate normality. Table 1 Analysis Type Example Parametric Procedure Compare means between two distinct/independent groups Compare two quantitative measurements taken from the same individual Compare means between three or more distinct/independent groups Estimate the degree of association between two quantitative variables Is the mean systolic blood pressure (at baseline) for patients assigned to placebo different from the mean for patients assigned to the treatment group? Was there a significant change in systolic blood pressure between baseline and the six-month followup measurement in the treatment group? If our experiment had three groups (e.g., placebo, new drug #1, new drug #2), we might want to know whether the mean systolic blood pressure at baseline differed among the three groups? Is systolic blood pressure associated with the patient s age? Two-sample t- Paired t- Analysis of variance (ANOVA) Pearson coefficient of correlation Nonparametric Procedure Wilcoxon ranksum Wilcoxon signedrank Kruskal-Wallis Spearman s rank correlation An example Suppose you have a sample of critically ill patients. The sample contains 20 female patients and 19 male patients. The variable of interest is hospital length of stay (LOS) in

3 days, and you would like to compare females and males. The histograms of the LOS variable for males and females appear in Figure 1. We see that the distribution for females has a strong right skew. Notice that the mean for females is 60 days while the median is 31.5 days. For males, the distribution is more symmetric with a mean and median of 30.9 days and 30 days, respectively. Comparing the two groups, their medians are quite similar, but their means are very different. This is a case where the assumption of normality associated with a parametric is probably not reasonable. A nonparametric procedure would be more appropriate. This is the situation listed in the first row of Table 1 comparing means between two distinct groups. Thus, the appropriate nonparametric procedure is a Wilcoxon rank-sum. This would give us a p-value of Those of you familiar with p-values know that we typically compare our p-value to the value We usually say that a p-value less than 0.05 is an indication of a statistically significant result. So, we would say that there is no significant difference between the genders with respect to length of stay based on the Wilcoxon rank-sum. Incidentally, the p-value for the two-sample t-, which is the parametric procedure that assumes approximate normality, is You can see that in certain situations parametric procedures can give a misleading result. Figure 1 Why don t we always use nonparametric s? Although nonparametric s have the very desirable property of making fewer assumptions about the distribution of measurements in the population from which we drew our sample, they have two main drawbacks. The first is that they generally are less

4 statistically powerful than the analogous parametric procedure when the data truly are approximately normal. Less powerful means that there is a smaller probability that the procedure will tell us that two variables are associated with each other when they in fact truly are associated. If you are planning a study and trying to determine how many patients to include, a nonparametric will require a slightly larger sample size to have the same power as the corresponding parametric. The second drawback associated with nonparametric s is that their results are often less easy to interpret than the results of parametric s. Many nonparametric s use rankings of the values in the data rather than using the actual data. Knowing that the difference in mean ranks between two groups is five does not really help our intuitive understanding of the data. On the other hand, knowing that the mean systolic blood pressure of patients taking the new drug was five mmhg lower than the mean systolic blood pressure of patients on the standard treatment is both intuitive and useful. In short, nonparametric procedures are useful in many cases and necessary in some, but they are not a perfect solution. Take-home points Here is a summary of the major points and how they might affect statistical analyses you perform: Parametric and nonparametric are two broad classifications of statistical procedures. Parametric s are based on assumptions about the distribution of the underlying population from which the sample was taken. The most common parametric assumption is that data are approximately normally distributed. Nonparametric s do not rely on assumptions about the shape or parameters of the underlying population distribution. If the data deviate strongly from the assumptions of a parametric procedure, using the parametric procedure could lead to incorrect conclusions. You should be aware of the assumptions associated with a parametric procedure and should learn methods to evaluate the validity of those assumptions. If you determine that the assumptions of the parametric procedure are not valid, use an analogous nonparametric procedure instead. The parametric assumption of normality is particularly worrisome for small sample sizes (n < 30). Nonparametric s are often a good option for these data. It can be difficult to decide whether to use a parametric or nonparametric procedure in some cases. Nonparametric procedures generally have less power for the same sample size than the corresponding parametric procedure if the data truly are normal. Interpretation of nonparametric procedures can also be more difficult than for parametric procedures. Visit with a statistician if you are in doubt about whether parametric or nonparametric procedures are more appropriate for your data. The book Practical Nonparametric Statistics 2 is an excellent resource for anyone interested in learning about this topic in great detail. More general texts such as

5 Fundamentals of Biostatistics 3 and Intuitive Biostatistics 4 have chapters covering the topic of nonparametric procedures. More information The Mayo Clinic CTSA provides a biostatistical consulting service through its BERD Resource. More information can be found on the BERD home page. References 1. Walsh, J.E. (1962) Handbook of Nonparametric Statistics, New York: D.V. Nostrand. 2. Conover, W.J. (1980). Practical Nonparametric Statistics, New York: Wiley & Sons. 3. Rosner, B. (2000). Fundamentals of Biostatistics, California: Duxbury Press. 4. Motulsky, H. (1995). Intuitive Biostatistics, New York: Oxford University Press.

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