Non-Parametric Inference Procedures
Non-parametric inference procedures were developed to be used in cases when the distribution of the variable of interest in the population is known to be not-normal, or not known at all, and furthermore the distributional assumptions relevant to parametric statistical inference are undetermined (hence the name nonparametric).
Nonparametric tests are also referred to as tests. These tests have the obvious advantage of not requiring the assumption of normality or the assumption of homogeneity of variance.
They compare medians rather than means and, as a result, if the data have one or two outliers, their influence is negated.
Parametric tests are preferred because, in general, for the same number of observations, they are more likely to lead to the rejection of a false hull hypothesis. That is, they have more power.
This greater power stems from the fact that if the data have been collected at an interval or ratio level, information is lost in the conversion to ranked data (i.e., merely ordering the data from the lowest to the highest value).
Non-Parametric Tests Many statistical tests assume that you have sampled data from populations that follow a Normal distribution. Biological data never follow a Gaussian distribution precisely, because a Gaussian distribution extends infinitely in both directions, and so it includes both infinitely low negative numbers and infinitely high positive numbers. But many kinds of biological data follow a bell-shaped distribution that is approximately Gaussian. Because statistical tests work well even if the distribution is only approximately Gaussian (especially with large samples), these tests are used routinely in many fields of science.
An alternative approach does not assume that data follow a Gaussian distribution. These tests, called nonparametric tests, are appealing because they require fewer assumptions about the distribution of the data.
In this approach, values are ranked from low to high, and the analyses are based on the distribution of ranks. Often, the analysis will be one of a series of experiments. Since you want to analyze all the experiments the same way, you cannot rely on the results from a single normality test.