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Misuse of null hypothesis significance testing: would estimation of positive and negative predictive values improve certainty of chemical risk assessment?

  • Mirco Bundschuh
  • , Michael C. Newman
  • , Jochen P. Zubrod
  • , Frank Seitz
  • , Ricki R. Rosenfeldt
  • , Ralf Schulz

Publication: Contribution to journalJournal articlepeer-review

Abstract

Although generally misunderstood, the p value is the probability of the test results or more extreme results given H-0 is true: it is not the probability of H-0 being true given the results. To obtain directly useful insight about H-0, the positive predictive value (PPV) and the negative predictive value (NPV) may be useful extensions of null hypothesis significance testing (NHST). They provide information about the probability of statistically significant and non-significant test outcomes being true based on an a priori defined biologically meaningful effect size. The present study explores the utility of PPV and NPV in an ecotoxicological context by using the frequently applied Daphnia magna reproduction test (OECD guideline 211) and the chemical stressor lindane as a model system. The results indicate that especially the NPV deviates meaningfully between a test design strictly following the guideline and an experimental procedure controlling for alpha and beta at the level of 0.05. Consequently, PPV and NPV may be useful supplements to NHST that inform the researcher about the level of confidence warranted by both statistically significant and non-significant test results. This approach also reinforces the value of considering alpha, beta, and a biologically meaningful effect size a priori.
Original languageEnglish
Pages (from-to)7341-7347
Number of pages7
JournalEnvironmental Science and Pollution Research
Volume20
Issue number10
DOIs
Publication statusPublished - 2013

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Sample size
  • Bayesian
  • Power analysis
  • Effect size
  • Type I error rate
  • Type II error rate

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