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THE WILD, WILD, WET!

THE WILD, WILD, WET!. SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation. THE PERFECT WORLD. FOCUS ON DATA ANALYSIS. STEP 1: GRAPH THE DATA STEP 2: Analyze the data by EPA flowcharts STEP 3: DO THE RESULTS MAKE SENSE?. SOFTWARE PROGRAMS.

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THE WILD, WILD, WET!

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  1. THE WILD, WILD, WET! SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

  2. THE PERFECT WORLD

  3. FOCUS ON DATA ANALYSIS • STEP 1: GRAPH THE DATA • STEP 2: Analyze the data by EPA flowcharts • STEP 3: DO THE RESULTS MAKE SENSE?

  4. SOFTWARE PROGRAMS • Many software packages/programs are available • DO NOT assume they follow the EPA recommended analysis • DO verify the software by running example datasets from the methods manuals

  5. STATISTICAL AND BIOLOGICAL SIGNIFICANCE SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

  6. TOXIC VS. NON-TOXIC • How are data from a WET test used to make a decision of toxicity? • Two paths: • Decision based on the observed result • Decision based on standard effect

  7. WHO DECIDES WHICH PATH? • The Permit Writers • BOTH approaches are supported by the TSD and the methods manual

  8. OBSERVED RESULT • Data from the test are used to determine if toxicity is present by hypothesis testing • HO: Effluent is not toxic • Ha: Effluent is toxic

  9. STANDARD EFFECT • A preselected level of effect is considered toxic • Acute test: 50 % effect • Chronic test: 25 % effect

  10. THERE ARE INHERENT STRENGTHS AND WEAKNESSES TO BOTH APPROACHES

  11. COMPONENTS WHICH IMPACT THE NOEC

  12. WHAT BIOLOGICAL CONCLUSIONS CAN BE MADE FROM THE STATISTICAL ANALYSIS OF A SINGLE TOXICITY TEST? The biological impact was significant in the beaker

  13. THE LESS THAN PERFECT WORLD

  14. INTRA- AND INTER-TEST VARIABILITY SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

  15. TYPES OF VARIABILITY • Intra-test : among and between concentrations • Inter-test: within one lab, same method • Inter-lab: between labs, same method • Method specific: within limits of method

  16. INTRA-TEST VARIABILITY

  17. INTRA-TEST VARIABILITY AND ENDPT. UNCERTAINTY

  18. SOURCES OF INTRA-TEST VARIABILITY • Genetic variability • Organism handling and feeding • Toxicity among and between treatments • Non-homogeneous sample source

  19. SOURCES OF INTRA-TEST VARIABILITY • Abiotic conditions • Dilution scheme • Number of organisms/treatment • Dilution water pathogens

  20. SOURCES OF INTER-TEST VARIABILITY • Intra-test sources • Analyst experience and practice • Organism age and health • Acclimation • Dilution water

  21. SOURCES OF INTER-TEST VARIABILITY • Sample quality • Test chamber characteristics

  22. SOURCES OF INTER-TEST VARIABILITY • Replicate volume • Procedures

  23. VARIABILITY AND POINT ESTIMATE UNCERTAINTY

  24. HIGH VARIABILITY - LOW STATISTICAL POWER

  25. LOW VARIABILITY - HIGH STATISTICAL POWER

  26. ACTIONS TO REDUCE VARIABILITY • Increase number of reps/treatment • QA program • Establish and follow strict procedures • Maximize analyst skill • Contract lab selection • Additional QA/QC criteria

  27. EXAMPLES OF ADDITIONAL QC TEST CRITERIA • Region IX: upper MSD limits • Washington: upper MSD limits, change in alpha • N. Carolina: limit control CVs, C. dubia “PSC” • Region VI: limit control CV, increase # replicates, biological significance

  28. SUSPICIOUS DATA AND OUTLIER DETECTION SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

  29. CONCERNS • Outliers make interpretation of WET data difficult by • Increasing the variability in test responses • Biasing mean responses

  30. IDENTIFYING OUTLIERS • Graph raw data, means and residuals

  31. IDENTIFYING OUTLIERS • Formal statistical test - Chauvenet’s Criterion • Using the previous mysid data, the critical values are: • Mean = .80, Std. Dev. = 0.302, n = 8 • Chauvenet’s Criterion Value = n/2 = 4 • Z-score = 2.054 (two-tailed probability of 4 %) • The calculations are: • Equation 1) (Z-score)(Std. Dev.) = (2.054)(0.302) = 0.620 • Mean  Equation 1 = 0.80  0.620 = 1.42 - 0.18 • Outlier Range is >1.42 or <0.18 • A value of 0.2 is not an outlier.

  32. CAN A CAUSE BE ASSIGNED TO THE OUTLIER(S) ? • Review analyst’s daily observations • Check water chemistry data • Check data entry • Check calculations • If cause can be assigned to outlier, then reanalyze data without outlier

  33. DETERMINE EFFECT ON TEST INTERPRETATION • Keep all data unless cause is found • Analyze data with and without suspect data • Determine effect of suspect data on test interpretation • Results reported will depend on effect of outlier(s) on test interpretation

  34. Insignificant Effect With Outlier IC25 = 131 (96.9-158) ppb NOEC = 100 ppb % MSD = 28.1 % Without Outlier IC25 = 124 (93.6-152) ppb NOEC = 100 ppb % MSD = 20.9 % Report results with suspect data included Significant Effect With Outlier IC25 = 131 (96.9-158) ppb NOEC = 100 ppb % MSD = 28.1 % Without Outlier IC25 = 106 (83.8-126) ppb NOEC = 50 ppb % MSD = 12.2 % Report results from both analyses REPORTING OF RESULTS

  35. HORMESIS ANDNON-MONOTONIC CONCENTRATION RESPONSES SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

  36. WHAT IS HORMESIS ? • Calabrese and Baldwin, 1998 • General concept • Occurrence • Typical Characteristics

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