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On Abandoning Hypothesis Testing in Environmental Standard Compliance Assessment
Song S Qian1, Robert J Miltner2
1Department of Environmental Sciences, The University of Toledo, 2801 West Bancroft Street, MS# 604, Toledo, OH, 43606-3390, USA. song.qian@utoledo.edu.
Environmental Management
|April 6, 2018
Summary
Hypothesis testing is inefficient for environmental compliance assessment due to small sample sizes and high data variability. A shift to statistical estimation offers a more effective probabilistic approach for water quality monitoring.
Area of Science:
- Environmental Science
- Environmental Monitoring
- Statistical Analysis
Background:
- Environmental standard compliance assessments often rely on hypothesis testing.
- Typical environmental assessments use small sample sizes and face high natural variability in water quality constituents.
- These conditions significantly reduce the statistical power of hypothesis tests.
Purpose of the Study:
- To argue for the abandonment of hypothesis testing in environmental standard compliance assessment.
- To highlight the inefficiencies and limitations of current hypothesis testing practices.
- To propose an alternative statistical estimation approach for improved environmental monitoring.
Main Methods:
- Analysis of basic characteristics of statistical significance tests.
- Illustration of problems using two case examples of environmental compliance assessment.
- Comparison of hypothesis testing with a statistical estimation approach.
Main Results:
- Hypothesis testing in environmental assessments often has low statistical power, leading to inefficient noncompliance detection.
- Detected noncompliance is frequently attributed to sampling or other errors rather than actual environmental issues.
- Current practices of assessing compliance water by water are insufficient to resolve these inherent problems.
Conclusions:
- The hypothesis testing framework is inadequate for environmental standard compliance assessment.
- A statistical estimation approach, utilizing probabilistic methods, is recommended for more effective environmental monitoring.
- Leveraging information from similar water assessments can enhance the reliability of compliance evaluations.
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