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Permutation/randomization-based inference for environmental data.

R Christopher Spicer1, Harry J Gangloff2

  • 1WCD Group LLC, 23 Route 31 North, Pennington, NJ, 08534, USA. RCSpicer@WCDGroup.com.

Environmental Monitoring and Assessment
|February 7, 2016
PubMed
Summary
This summary is machine-generated.

Environmental contaminant data analysis using traditional methods can be misleading. Permutation testing offers a more robust approach for accurate inference, especially with non-normal data, by focusing on detection frequencies.

Keywords:
Detection frequencyDistributionInferencePermutationRandomization

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Area of Science:

  • Environmental Science
  • Statistical Inference
  • Ecotoxicology

Background:

  • Quantitative inference for environmental contaminants typically relies on the Neyman/Pearson (N/P) hypothesis-testing model.
  • This model uses the mean as the primary measure but is limited by assumptions of random sampling and data normality.
  • Alternative methods like permutation/randomization inference, proposed by R. A. Fisher, derive probability from occurrence proportions, independent of data distribution or sampling methods.

Purpose of the Study:

  • To compare the inference generated by traditional Neyman/Pearson (N/P) hypothesis testing with permutation/randomization-based inference.
  • To demonstrate how traditional methods can lead to misleading conclusions in environmental contaminant analysis.
  • To highlight the utility of permutation/randomization methods, particularly using differences in frequency of detection (Δf d), for more reliable inference.

Main Methods:

  • Analysis of environmental contaminant data, including airborne fungi, asbestos in dust, and 1,2,3,4-tetrachlorobenzene (TeCB) in soil.
  • Application of traditional Neyman/Pearson (N/P) hypothesis testing based on means and variance.
  • Application of permutation/randomization-based inference, focusing on the difference in frequency of detection (Δf d).
  • Validation using bootstrapping and permutation testing to confirm p-values derived from Δf d.

Main Results:

  • Traditional N/P hypothesis testing based on means/variance yielded potentially misleading inferences for environmental contaminant data.
  • Permutation/randomization inference using differences in frequency of detection (Δf d) provided more accurate and reliable quantitative conclusions.
  • Bootstrapping and permutation testing confirmed the validity of p-values calculated via Δf d, supporting the robustness of this approach.

Conclusions:

  • Permutation/randomization-based inference, especially using Δf d, offers a more reliable alternative to traditional N/P hypothesis testing for environmental contaminant data.
  • This approach is particularly valuable when data do not meet assumptions of normality or random sampling.
  • The use of bootstrapping and permutation testing is recommended to verify the appropriateness of statistical models and ensure robust environmental data analysis.