Nonparametric Tests are all studies involving ranked data, i.e., data that can be put in order and used when there is no assumption that the population has a normal distribution or when the population is known to have low and high values and valuable in detecting population differences when certain assumptions are not satisfied.
Nonparametric tests are often used in place of their parametric counterparts when certain assumptions about the underlying population are questionable. For example, when comparing two independent samples, the Wilcoxon Mann-Whitney test does not assume the difference between the samples is normally distributed, whereas its parametric counterpart, the two-sample t-test, does. Nonparametric tests may be, and often are, more powerful in detecting population differences when certain assumptions are not satisfied. All tests involving ranked data (data that can be put in order) are nonparametric.