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Related Concept Videos

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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P-value is one of the most crucial concepts in statistics.
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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Critical Region, Critical Values and Significance Level01:16

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
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Environmental pollution impacts: Are p values over-valued?

Evgenios Agathokleous1

  • 1School of Applied Meteorology, Nanjing University of Information Science and Technology (NUIST), Ningliu Rd. 219, Nanjing, Jiangsu 210044, China.

The Science of the Total Environment
|August 7, 2022
PubMed
Summary
This summary is machine-generated.

Environmental science studies often overemphasize p values, neglecting effect sizes. Reporting effect sizes alongside p values provides more meaningful insights into pollution impacts and organism responses.

Keywords:
Contamination effectsDose-response assessmentEcological effectsEnvironmental pollutionOrganismic stressStatistical testing

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Area of Science:

  • Environmental Science
  • Ecotoxicology
  • Statistical Ecology

Background:

  • Published environmental science literature is dominated by p-value reporting.
  • Effect size reporting is neglected in primary data publications, despite its importance.

Purpose of the Study:

  • To highlight the limitations of p-values in environmental science.
  • To advocate for the increased use and reporting of effect sizes and confidence intervals.

Main Methods:

  • Literature review analysis focusing on statistical reporting practices.
  • Discussion of the interpretation and limitations of p-values versus effect sizes.

Main Results:

  • P-values are prevalent, but often insufficient for fully describing pollution impacts.
  • Effect sizes offer more informative insights into pollution effects, organism susceptibility, and pollutant potency.

Conclusions:

  • Statistical significance (p-values) does not equate to biological or practical significance.
  • Reporting effect sizes and confidence intervals is crucial for integrated understanding of pollution impacts.
  • Shifting language from 'statistical significance' to 'evidence' can prevent public and policy misinterpretation.