The basis of non-parametric tests

The Basis of Non-parametric Tests

Conceptual Understanding

  • These tests are also known as distribution-free tests as they do not rely on certain distributional assumptions.
  • They make minimal assumptions about the nature or parameters of the population distribution such as its mean or standard deviation.
  • Their aim is to test hypotheses which are not about specific parameters (i.e., non-parametric).

Types of Data

  • Non-parametric tests are applicable to ordinal, nominal, and ranked data.
  • They are not restricted to numeric data, and can also be used with categorical data.
  • However, they require that the data is measurable.

Conditions to Use Non-parametric Tests

  • They are especially useful when the assumptions of parametric counterparts, like t-tests or ANOVA, are violated.
  • It’s suitable if the data isn’t normally distributed or if there’s non-constant variability.
  • They’re a good fit if there are outliers present in the data, as they’re less sensitive to them.
  • They also provide an avenue when the data is on a rank, score, or ordinal scale.

Working Principle

  • Non-parametric tests generally function by ranking the data from smallest to largest and evaluating the ranks.
  • These tests focus more on the order or ranking of data rather than the specific data values themselves.
  • They’re essentially comparing the median rather than the mean values, making them resistant to outliers.


  • A drawback is that non-parametric tests can be less powerful than their parametric counterparts. This means they are sometimes unable to detect a significant effect even if it truly exists.
  • Another limitation is that they cannot provide detailed insight into the differences between groups compared to parametric tests.
  • Lastly, while they avoid a few assumptions, they still require certain assumptions to be met (like homogeneous variance or independent observations), hence are not completely assumption-free.