https://clincalc.com/stats/samplesize.aspx.

Rules of thumb: Some approximate sample-size calculations are simple enough to do on a scrap

of paper or even in your head! You find some of these in Chapter 25.

Going Outside the Norm with Nonparametric

Statistics

All statistical tests are derived on the basis of some assumptions about your data. Most of the classical

significance tests, including Student t tests, analysis of variance (ANOVA), and regression tests,

assume that your data are distributed according to some classical sampling distribution, which is also

called a frequency distribution. Most tests assume your data has a normal distribution (see Chapter

24). Because the classic distribution functions are all written as mathematical expressions involving

parameters (like means and standard deviation), they’re called parametric distribution functions.

Parametric tests assume that your data conforms to a parametric distribution function. Because the

normal distribution is the most common statistical distribution, the term parametric test is often used

to mean a test that assumes normally distributed data. But sometimes your data don’t follow a

parametric distribution. For example, it may be very noticeably skewed, as shown in Figure 3-5a.

Sometimes, you may be able to perform a mathematical transformation of your data to make it more

normally distributed. For example, many variables that have a skewed distribution can be turned into

normally distributed numbers by taking logarithms, as shown in Figure 3-5b. If, by trial and error, you

can find some kind of transformation that normalizes your data, you can run the classical tests on the

transformed data, as described in Chapter 9.

© John Wiley & Sons, Inc.

FIGURE 3-5: Skewed data (a) can sometimes be turned into normally distributed data (b) by taking logarithms.