BASICS OF WET STATISTICS

2. BASICS OF WET STATISTICS SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

5. GRAPH THE DATA

6. ANALYZE DATA FOLLOWING EPA WET STATISTICAL FLOWCHARTS • Hypothesis Tests • NOAEC (Acute) • NOEC (Chronic) • Point Estimation • LC50 (Acute) • EC25 or IC25 (Chronic)

7. PURPOSE OF HYPOTHESIS TESTS AND BASIC CONSIDERATIONS • Purpose - Determine if two things (responses) are different • Relevance of initial (control) condition(s) • Power of statistical test

8. EFFECTS ASSOCIATED WITH THE NOEC IN FATHEAD MINNOW GROWTH DATA

9. Test assumptions of ANOVA Transform data if necessary Normally distributed data Shapiro-Wilks Test Variance is equal Bartlett’s test Select appropriate test Parametric Tests Assumptions met Non-Parametric Tests Assumptions NOT met EPA HYPOTHESIS TEST FLOWCHART (MULTI-CONC)

10. MULTIPLE CONCENTRATION PARAMETRIC TESTS • Dunnett’s Test • Equal number of replicates in each treatment • Multiple t-tests with Bonferroni adjustment • Unequal number of replicates in each treatment

11. MULIPLE CONCENTRATION NON-PARAMETRIC TESTS • Steel’s Many-one Rank Test • Equal number of replicates in each treatment • Wilcoxon Rank Sum • Unequal number of replicates in each treatment

12. PASS/FAIL TESTS • Control and critical concentration (IWC) • Test assumptions • Transformations - Arc sine square root • Normality - Shapiro-Wilk’s test • Homogeneity - F-test • Test for statistical difference • Normal/homogeneous - t-test • Non-normal - Wilcoxon rank sum test • Normal/heterogeneous - Modified t-test

13. PURPOSE OF POINT-ESTIMATIONAND BASIC CONSIDERATIONS • Describe relationship between two parameters • Selection of a significant response • Elucidation of relationship • Confidence in relationship

14. EPA POINT-ESTIMATE METHOD SELECTION • Binomial Data • Probit • Spearman-Karber • Untrimmed or trimmed • Graphical • Continuous Data • ICp / Linear Interpolation

15. PROBIT ANALYSIS • Binomial data only (two choices) • Dead or alive, normal/abnormal, etc. • Normally distributed • Adjusted for control mortality • Abbott’s correction • At least two partial mortalities • Sufficient fit • Chi-square test for heterogeneity • Designed for LC50/EC50 and confidence intervals

16. SPEARMAN-KARBER • Nonparametric model • Monotonic concentration response • Smoothing • Adjusted for control mortality • Zero response in the lowest concentration • 100% response in the highest concentration • Calculates LC50/EC50 • Confidence interval calculation requires at least one partial response

17. TRIMMED SPEARMAN-KARBER • Same basic procedure as Spearman-Karber • Requires at least 50% mortality in one concentration • The trimming procedure is employed when the zero and/or 100% response requirements of Spearman-Karber method are not met.

18. GRAPHICAL METHOD • Specifics • Nonparametric procedure • Adjusted for control mortality • Monotonic concentration response • Smoothing • Linear interpolation of “all or nothing” response • Calculates LC50/EC50 - No CI’s

19. INHIBITION CONCENTRATION (ICp) • Specifics • Nonparametric procedure • Calculates any effect level • Monotonic concentration response • Smoothing • Random, independent, and representative data • Piecewise linear interpolation • Bootstrapped confidence intervals

20. 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

21. DO THE RESULTS MAKE SENSE ???

22. TOXIC UNITS IN WET TESTS • Goals 1) Standardize the results of toxicity tests to simulate chemical specific criteria. 2) Create a reporting value which increases with sample toxicity.

23. DEFINITIONS OF TU VALUES • Acute • TUa = 100/LC50 OR • Chronic • TUc = 100/NOEC • where the NOEC is defined by hypothesis testing or the IC25

24. SUMMARY OF THE ANALYSIS OF WET DATA • STEP 1: Graph The Data • STEP 2: Analyze The Data By EPA Methods • STEP 3: Do The Results Make Sense?

27. ANALYSIS OF MULTIPLE CONTROL TOXICITY TESTS SETAC Expert Advisory Panel Performance Evaluation and Data Interpretation

28. WHAT IS A CONTROL SAMPLE ? • A treatment in a toxicity test that duplicates all the conditions of the exposure treatments but contains no test material. The control is used to determine the absence of toxicity of basic test conditions (e.g. health of test organisms, quality of dilution water). Rand and Petrocelli, 1985.

29. WHAT IS A REFERENCE SAMPLE? • “A reference sample is the “control” by which to gauge the instream effects of a discharge at a particular site.” Grothe et.al. 1996. - site-specific - ecoregional

30. WHEN ARE MULTIPLE CONTROLS USED? • When manipulations are made to SOME of the test concentrations or treatments. • To compare “standard” and “alternative” methods. • When testing control and/or reference samples in which the quality is unknown. • When a sample used for toxicity testing possess physico-chemical properties significantly different from water in which surrogate test organisms were cultured. • TIEs - Toxicity Identification Evaluations.

31. WHEN ARE MULTIPLE CONTROLS USED? Example #1 • When manipulations are made to SOME of the test concentrations or treatments.

32. BRINE ADDITION IN MARINE TESTS Concentration Effluent Volume Brine Volume Seawater Volume Salinity ( 0 ppt) (68 ppt) (34 ppt)Seawater 0 ml 0 ml 1000 ml 34 ppt Control1.25 % 12.5 ml 0 ml 987.5 ml 34 ppt2.5 % 25 ml 0 ml 975 ml 33 ppt5 % (IWC) 50 ml 0 ml 950 ml 32 ppt 10 % 100 ml 100 ml 800 ml 34 ppt20 % 200 ml 200 ml 600 ml 34 pptBrine 0 ml 200 ml 600 ml 34 pptControl + 200 ml

33. ANALYSIS OF TWO-CONTROL TOXICITY TESTS WHEN SOME CONCENTRATIONS WERE MANIPULATED

34. WHEN ARE MULTIPLE CONTROLS USED ? Example #2 • To compare “standard” and “alternative” methods. • To determine treatment effects.

35. EFFECT OF KELP STORAGE ON SENSITIVITY TO COPPER

36. WHEN ARE MULTIPLE CONTROLS USED? Example #3 • When testing control and/or reference samples in which the quality is unknown. - Use of a reference not previously tested (ambient). - Quality of reference may vary from season to season (ambient). - When the potential exists for a sample to be impacted or impaired.

37. EFFECT OF A NON-POINT DISCHARGE ON AN INSTREAM DILUTION WATER

38. WHEN ARE MULTIPLE CONTROLS USED ? Example #4 • When a sample used for toxicity testing possess physico-chemical properties significantly different from water in which surrogate test organisms were cultured - As a natural phenomenon - Due to sample manipulation

39. WHEN ARE MULTIPLE CONTROLS USED ? Example #5 • TIEs - Toxicity Identification Evaluations.- Methods require the use of multiple controls called “blanks” which are exact manipulations on the dilution water.

40. TAKE HOME POINTS • Multiple negative controls are a good idea if: - New reference or control sample. - Performing any sample manipulations. - Comparing “standard” vs. “alternative” methods. Multiple Positive Controls (e.g. Ref Tox tests) should be used in this situation - Using multiple organisms with different sensitivities.

41. REFERENCES: • Short-Term Methods For Estimating The Chronic Toxicity Of Effluents And Receiving Water To Freshwater Organisms. EPA-600-4-91-002. July, 1994. • Methods for Measuring the Acute Toxicity of Effluents and Receiving Waters to Freshwater and Marine Organisms. EPA/600/4-90/027F. August, 1993. - Have recommendations for multiple controls under certain conditions. • Methods for Aquatic Toxicology Identification Evaluations. Phase I Toxicity Characterization Procedures. EPA/600/6-91/003. February, 1991.- Has recommendations for multiple controls “blanks”. • Whole Effluent Toxicity Testing: An Evaluation of Methods and Prediction of Receiving Water System Impacts. Grothe et al.. 1996.

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

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

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

46. 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 n/2 = 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.

47. 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

48. 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, best professional judgement, and discussions with regulatory agency

49. 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