“BAD” DATA Sun e e e e . Overview ä Bad Data ä Learning from unexpected results ä Isotherm...
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Transcript of “BAD” DATA Sun e e e e . Overview ä Bad Data ä Learning from unexpected results ä Isotherm...
OverviewOverview
Bad Data Learning from unexpected results Isotherm Analysis
Bad Data Learning from unexpected results Isotherm Analysis
Sources of “Bad” DataSources of “Bad” Data Error in preparation of samples
mass or volume measurement error contamination improper storage sample substitution sample loss samples with high heterogeneity
Apparatus failures leaks incompatible materials inadequate control of an important parameter
Error in preparation of samples mass or volume measurement error contamination improper storage sample substitution sample loss samples with high heterogeneity
Apparatus failures leaks incompatible materials inadequate control of an important parameter
Instrument ErrorsInstrument Errors
detector malfunction below detection limit or above maximum interference software (instrument or computer) hardware (analog to digital converter, power
supply,...) calibration
detector malfunction below detection limit or above maximum interference software (instrument or computer) hardware (analog to digital converter, power
supply,...) calibration
More sources of “Bad Data”More sources of “Bad Data”
Error in data analysis numerical error (data entry) units (classic errors of factors of 10 and factors of
1000) incorrectly applied theory
Error in theory
Error in data analysis numerical error (data entry) units (classic errors of factors of 10 and factors of
1000) incorrectly applied theory
Error in theory
Bad Data aren’t Bad!
“Bad” data usually means the results were unexpected perhaps unorthodox!
Copernicus “Concerning the Revolutions of the Celestial Bodies”1543
Papal Index of forbidden books until 1835
_____________________
Data do not lie! Data always mean something If you ignore data that you don’t understand you are
missing an opportunity to learn
Bad data for 292 years!
Unexpected Results
Lack of repeatability (poor precision) scatter for all data outlier systemic error
0123456789
10
0 2 4 6 8 10
distance from source (m)
Con
cen
trati
on
(m
g/L
)
measured concentration
expected concentration
0123456789
10
0 2 4 6 8 10
distance from source (m)
Con
cen
trat
ion
(m
g/L
)
measured concentration
expected concentration
0123456789
10
0 2 4 6 8 10
distance from source (m)
Con
cen
trat
ion
(m
g/L
) measured concentration
expected concentration
Unexpected Results
Inconsistent with theory mass balances indicate loss or gain of mass inconsistent with previous results some “theories” are only hypotheses
Sun ee Sun
02468
101214161820
0 2 4 6 8 10
parameter A
Par
amet
er B
measured trend
expected trend
Responses to Unexpected Results
Determine accuracy of technique by analyzing known samples
Determine precision of technique by analyzing replicates Evaluate propagation of errors through analysis
are you trying to measure the difference between two large numbers?
is the precision of the measurement similar to the magnitude of the estimate?
Are you not controlling an important parameter? Is the parameter that you are studying insignificant?
Isotherm Analysis Pointers Units
Express mass of VOC in grams Express concentrations as g per mL Remember GC injection volume was 0.1 mL
Use names to keep track of parameters in spreadsheet
Build sheet from left to right
More PointersMore Pointers
Soil density = 1.6 g/mL Soil moisture content is 10.7% Soil mass was close to 20 g Analyze data sets as sets You will get 6 estimates for each parameter. Where do all these parameters come from?
Soil density = 1.6 g/mL Soil moisture content is 10.7% Soil mass was close to 20 g Analyze data sets as sets You will get 6 estimates for each parameter. Where do all these parameters come from?
L
GL
GGGVOC
GS
GLS
L VH
CVCM
CM
HK
sc
Proposal for the VOC isotherm lab
Proposal for the VOC isotherm lab
Change Full Report to Spreadsheet Analyze all 6 sets of data (isotherm data
summary.xls) See which parameters are stable Calculate all parameters independently
(scenarios for each data set?) Extend due date until Friday of next week
Change Full Report to Spreadsheet Analyze all 6 sets of data (isotherm data
summary.xls) See which parameters are stable Calculate all parameters independently
(scenarios for each data set?) Extend due date until Friday of next week