1. “BAD” DATA Sun e e e e 

2. Overview ä Bad Data ä Learning from unexpected results ä Isotherm Analysis ä Bad Data ä Learning from unexpected results ä Isotherm Analysis

3. Sources of “Bad” Data ä Error in preparation of samples ä mass or volume measurement error ä contamination ä improper storage ä sample substitution ä sample loss ä samples with high heterogeneity ä Apparatus failures ä leaks ä incompatible materials ä inadequate control of an important parameter ä Error in preparation of samples ä mass or volume measurement error ä contamination ä improper storage ä sample substitution ä sample loss ä samples with high heterogeneity ä Apparatus failures ä leaks ä incompatible materials ä inadequate control of an important parameter

4. Instrument Errors ä detector malfunction ä below detection limit or above maximum ä interference ä software (instrument or computer) ä hardware (analog to digital converter, power supply,...) ä calibration ä detector malfunction ä below detection limit or above maximum ä interference ä software (instrument or computer) ä hardware (analog to digital converter, power supply,...) ä calibration

5. More sources of “Bad Data” ä Error in data analysis ä numerical error (data entry) ä units (classic errors of factors of 10 and factors of 1000) ä incorrectly applied theory ä Error in theory ä Error in data analysis ä numerical error (data entry) ä units (classic errors of factors of 10 and factors of 1000) ä incorrectly applied theory ä Error in theory

6. Bad Data aren’t Bad! ä “Bad” data usually means the results were unexpected ä perhaps unorthodox! ä Copernicus “Concerning the Revolutions of the Celestial Bodies”1543 ä Papal Index of forbidden books until 1835 ä _____________________ ä Data do not lie! ä Data always mean something ä If you ignore data that you don’t understand you are missing an opportunity to learn ä “Bad” data usually means the results were unexpected ä perhaps unorthodox! ä Copernicus “Concerning the Revolutions of the Celestial Bodies”1543 ä Papal Index of forbidden books until 1835 ä _____________________ ä Data do not lie! ä Data always mean something ä If you ignore data that you don’t understand you are missing an opportunity to learn Bad data for 292 years!

7. Unexpected Results ä Lack of repeatability (poor precision) ä scatter for all data ä outlier ä systemic error ä Lack of repeatability (poor precision) ä scatter for all data ä outlier ä systemic error

8. Unexpected Results ä Inconsistent with theory ä mass balances indicate loss or gain of mass ä inconsistent with previous results ä some “theories” are only hypotheses ä Inconsistent with theory ä mass balances indicate loss or gain of mass ä inconsistent with previous results ä some “theories” are only hypotheses Sun e e

9. Responses to Unexpected Results ä Determine accuracy of technique by analyzing known samples ä Determine precision of technique by analyzing replicates ä Evaluate propagation of errors through analysis ä are you trying to measure the difference between two large numbers? ä is the precision of the measurement similar to the magnitude of the estimate? ä Are you not controlling an important parameter? ä Is the parameter that you are studying insignificant? ä Determine accuracy of technique by analyzing known samples ä Determine precision of technique by analyzing replicates ä Evaluate propagation of errors through analysis ä are you trying to measure the difference between two large numbers? ä is the precision of the measurement similar to the magnitude of the estimate? ä Are you not controlling an important parameter? ä Is the parameter that you are studying insignificant?

10. Isotherm Analysis Pointers ä Units ä Express mass of VOC in grams ä Express concentrations as g per mL ä Remember GC injection volume was 0.1 mL ä Use names to keep track of parameters in spreadsheet ä Build sheet from left to right ä Units ä Express mass of VOC in grams ä Express concentrations as g per mL ä Remember GC injection volume was 0.1 mL ä Use names to keep track of parameters in spreadsheet ä Build sheet from left to right

11. More Pointers ä Soil density = 1.6 g/mL ä Soil moisture content is 10.7% ä Soil mass was close to 20 g ä Analyze data sets as sets ä You will get 6 estimates for each parameter. ä Where do all these parameters come from? ä Soil density = 1.6 g/mL ä Soil moisture content is 10.7% ä Soil mass was close to 20 g ä Analyze data sets as sets ä You will get 6 estimates for each parameter. ä Where do all these parameters come from?

12. Proposal for the VOC isotherm lab ä Change Full Report to Spreadsheet ä Analyze all 6 sets of data (isotherm data summary.xls) ä See which parameters are stable ä Calculate all parameters independently (scenarios for each data set?) ä Extend due date until Friday of next week ä Change Full Report to Spreadsheet ä Analyze all 6 sets of data (isotherm data summary.xls) ä See which parameters are stable ä Calculate all parameters independently (scenarios for each data set?) ä Extend due date until Friday of next week