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Permutation methods. Part II

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Permutation statistical methods possess a number of advantages compared with conventional statistical methods, making permutation statistical methods the preferred statistical approach for many research situations. Permutation statistical methods are data‐dependent, do not rely on distribution assumptions such as normality, provide either exact or highly‐accurate approximate probability values, do not require knowledge of theoretical standard errors, and are ideal methods for small data sets where theoretical mathematical functions are often poor fits to discrete sampling distributions. On the other hand, permutation statistical methods are computationally intensive. Computational efficiencies for permutation statistical methods are described and permutation statistical methods are illustrated with a variety of common statistical tests and measures.

This article is categorized under:

  • Statistical and Graphical Methods of Data Analysis > Bootstrap and Resampling
  • Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
  • Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
  • Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods

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Statistical and Graphical Methods of Data Analysis > Bootstrap and Resampling
Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods
Statistical and Graphical Methods of Data Analysis > Multivariate Analysis

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