Comparing Non-Normal Data Graphically and with Non-Parametric Tests

Not all data in this world is predictable in the exact same way, of course, and not all data can be modeled using the Gaussian distribution. There are times, when we have to make comparisons about data using one of many distributions that represent data which could show different patterns other than the familiar and comforting “bell curve” of the normal distribution pattern we’re used to seeing in business presentations and the media alike. For instance, here’s data from the Weibull distribution, plotted using different shape and scale parameters. A Weibull distribution has two parameters, shape and scale, which determine how it looks (which varies widely), and how spread out it is.


shape <- 1
scale <- 5
x<-rweibull(1000000,shape,scale)
hist(x, breaks = 1000, main = paste("Weibull Distribution with shape: ",shape,", and scale: ",scale))
abline (v = median(x), col = "blue")
abline (v = scale, col = "red")
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Shape = 1; Scale = 5. The red line represents the scale value, and the blue line, the median of the data set.

Here’s data from a very different distribution, which has a scale parameter of 100.

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Shape = 1; Scale = 100. Same number of points. The red and blue lines mean the same things here too.

The shape parameter, as can be seen clearly here, is called so for a good reason. Even when the scale parameter changes wildly (as in our two examples), the overall geometry of our data looks similar – but of course, it isn’t. The change in the scale parameter has changed the probability of an event x ->0 towards the lower end of the x range (closer to zero), compared to an event x>>>0 further away. When you superimpose these distributions and their medians, you can get a very different picture of them.

If we have two very similar data sets like the data shown in the first graph and the data in the second, what kinds of hypothesis tests can we use? It is a pertinent question, because at times, we may not know that a data set may represent a process that can be modeled by a specific kind of distribution. At other times, we may have entirely empirical distributions represented by our data. And we’d still want to make comparisons using such data sets.

shape <- 1
scale1 <- 5
scale2<-scale1*2
x<-rweibull(1000000,shape,scale1)
xprime<-rweibull(1000000,shape,scale2)
hist(x, breaks = 1000, border = rgb(0.9,0.2,0.2,0.2), col = rgb(0.9,0.2,0.2,0.2), main = paste("Weibull Distribution different shape parameters: ",shape/100,", ", shape))
hist(xprime, breaks = 1000, border = rgb(0.2,0.9,0.2,0.2), col = rgb(0.2,0.9,0.2,0.2), add = T)
abline (v = median(x), col = "blue")
abline (v = scale, col = "red")

Different scale parameters. Red and blue lines indicate medians of the two data sets.
Different scale parameters. Red and blue lines indicate medians of the two data sets.

The Weibull distribution is known to be quite versatile, and can at times be used to approximate the Gaussian distribution for real world data. An example of this is the use of the Weibull distribution to approximate constant failure rate data in engineering systems. Let’s look at data from a different pair of distributions with a different shape parameter, this time, 3.0.

shape <- 3
scale1 <- 5
scale2<-scale1*1.1 #Different scale parameter for the second data set
x<-rweibull(1000000,shape,scale1)
xprime<-rweibull(1000000,shape,scale2)
hist(x, breaks = 1000, border = rgb(0.9,0.2,0.2,0.2), col = rgb(0.9,0.2,0.2,0.2), main = paste("Weibull Distribution different scale parameters: ",scale1,", ", scale2))
hist(xprime, breaks = 1000, border = rgb(0.2,0.9,0.2,0.2), col = rgb(0.2,0.9,0.2,0.2), add = T)
abline (v = median(x), col = "blue")
abline (v = median(xprime), col = "red")
Weibull distribution data - different because of scale parameters. Vertical lines indicate medians.
Weibull distribution data – different because of scale parameters. Vertical lines indicate medians.

The medians can be used to illustrate the differences between the data, and summarize the differences observed in the graphs. However, when we know that a data set is non-normal, we can adopt non-parametric methods from the hypothesis testing toolbox in R. Like hypothesis tests for normally distributed data that have comparable means, we can compare the medians of two or more samples of non-normally distributed data. Naturally, the same conditions – of larger samples of data being better, at times, apply. However, the tests can help us analytically differentiate between two similar-looking data sets. Since the Mann-Whitney median test and other non-parametric tests don’t make assumptions about the parameters of the underlying distribution of the data, we can rely on these tests to a greater extent when studying the differences between samples that we think may have a greater chance of being non-normal (even though the normality tests may say otherwise).

Non-parametrics and the inferential statistics approach
Non-parametrics and the inferential statistics approach: how to use the right test

When we conduct the AD test for normality on the two samples in question, we can see how these samples return a very low p-value each. This can also be confirmed using the qqnorm plots.

Let’s use the Mann-Whitney test for two medians from samples of non-normal data, to assess the difference between the median values. We’ll use a smaller sample size for both, and use the wilcox.test() command. For two samples, the wilcox.test() command actually performs a Mann-Whitney test.

shape <- 3
scale1 <- 5
scale2<-scale1*1.01
x<-rweibull(1000000,shape,scale1)
xprime<-rweibull(1000000,shape,scale2)
library(nortest)
paste("Normality test p-values: Sample 'x' ",ad.test(x)$p.value, " Sample 'xprime': ", ad.test(xprime)$p.value)

hist(x, breaks = length(x)/10, border = rgb(0.9,0.2,0.2,0.05), col = rgb(0.9,0.2,0.2,0.2), main = paste("Weibull Distribution different scale parameters: ",scale1,", ", scale2))
hist(xprime, breaks = length(xprime)/10, border = rgb(0.2,0.9,0.2,0.05), col = rgb(0.2,0.2,0.9,0.2), add = T)
abline (v = median(x), col = "blue")
abline (v = median(xprime), col = "red")
wilcox.test(x,xprime)
paste("Median 1: ", median(x),"Median 2: ", median(xprime))

Observe how close the scale parameters of both samples are. We’d expect both samples to overlap, given the large number of points in each sample. Now, let’s see the results and graphs.

Nearly overlapping histograms for the large non-normal samples
Nearly overlapping histograms for the large non-normal samples

The results for this are below.

Mann-Whitney test results
Mann-Whitney test results

The p-value here (for this considerable sample size) clearly illustrates the present of a significant difference. A very low p-value in this test result indicates that, if we were to make the assumption that the medians of these data sets are equal, there would be an extremely small probability, that we would see samples as extreme as observed in these samples. The fine difference in the medians observed in the median results can also be picked up in this test.

To run the Mann-Whitney test with a different confidence level (or significance), we can use the following syntax:

wilcox.test(x,xprime, conf.level = 0.95)

Note 1 : The mood.test() command in R performs a two-samples test of scale. Since the scale parameters in these samples of data we generated (for the purposes of the demo) are well known, in real life situations, the p-value should be interpreted based on additional information, such as the sample size and confidence level.

Note 2:  The wilcox.test() command performs the Mann Whitney test. This is a comparison of mean ranks, and not of the medians per se.

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