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Transcript of 12.Statistics GREY
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Unit 4
Statistics,
Detection Limits
and UncertaintyExperts Teaching from Practical Experience
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Unit 4 – Topics
Statistical Analysis
Detection Limits
Decision thresholds & detection levels
Instrument Detection Limits vs. Method Detection
Limits
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Unit 4 – Topics
Uncertainty Requirements
Guide to Uncertainty in Measurement (GUM)
Uncertainty budgets
Sampling uncertainty
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Unit 4 – Learning Objectives
Describe the responsibilities of the user of the
standards with regard to Detection Limits
Identify the guidance on analysis of samples and
reporting of results
Describe the changes in the guidance onestimating and reporting uncertainties
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Unit 4a
Statistics
Experts Teaching from Practical Experience
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MARSSIM (Chapter 8) recommends the followingsequence of steps:
1. Review the Data Quality Objectives (DQOs) and
Sampling Design
2. Conduct a Preliminary Data Review
3. Select the Tests
4. Verify the Assumptions of the Tests
5. Draw Conclusions from the Data
More detail is given in EPA QA/G-9S
Interpretation of Survey Results
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Review the objectives of the study
Translate the objectives into statistical hypotheses
Translate the objectives into limits on Type I & Type
II errors Review the sampling design & note any special
features or potential problems
Review the DQOs and Sampling Design
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Review Quality Assurance reports• Look for problems or anomalies
Calculate basic statistical quantities
• Calculate percentiles, measures of central tendency,dispersion and, if the data involves two variables, the
correlation coefficient
Graph the data
• Select graphical representations that illuminate the
structure of the data
Conduct a Preliminary Data Review
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Select the statistical method• Follow the “Decision Tree” from EPA QA/G-9S
Identify assumptions underlying the test
• List the key underlying assumptions such asdistributional form, dispersion, independence, etc
• Note any sensitive assumptions where relatively small
deviations could jeopardize the validity of the test
Select the Statistical Methods
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Determine approach for verifying assumptions• Review (or develop) a statistical model for the data
• Select the methods for verifying the assumptions
Perform tests of assumptions• Adjust for distributional assumptions (if warranted)
• Perform the calculations required for the tests
Determine corrective actions
Verify the Assumptions
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Determine corrective actions (if required) • Determine if data transformation will correct the
problem
• If data is missing, explore collecting more data or using
theoretical justification
• Consider robust procedures or nonparametric
hypothesis tests
Verify the Assumptions
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Perform the statistical procedure• Perform & document the statistical tests
• Identify outliers and recalculate if necessary
Draw study conclusions• If the null hypothesis is rejected, draw conclusions
& document
• If the null hypothesis is not rejected, verify limits on
decision errors then draw conclusions & document
• Interpret the results
Draw Conclusions from the Data
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Evaluate performance of the sampling design• Evaluate the statistical power of the design over the full
range of parameter values
Draw Conclusions from the Data
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Unit 4b
Detection Limits
Experts Teaching from Practical Experience
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Both N228.4-10 & N2885.-11 caution thatterminology and the definitions used for these
concepts (non-detect level & detection limit) are not
always consistent. It is the responsibility of the user
to understand, document, and justify the detectionlimit reported by any laboratory engaged to perform
analyses.
Definitions
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Definitions
Organization or Discipline LC LD
CSA N288.4/5 Non-detect Level (LC) Detection Limit (LD)
Health Physics
(ANSI/HPS N13.30-1996)Critical Level (CL) Lower Limit of Detection (LLD)
Occupational Hygiene
(AIHA, IOHA)Limit of Detection (LOD) Limit of Quantification (LOQ)
Environmental Analytical
Chemistry Cmte. of ACSLimit of Detection (LOD) Limit of Quantification (LOQ)
IUPAC Detection Decisions (LC)Minimum Detectable Value or
Detection Limit (LD)
ISO 11929:2010 Decision Threshold (y*) Detection Limit (y#)
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Non-detect level — the level below whichquantitative results are not obtained from the
measurement system or analysis method selected.
• The non-detect level is the smallest value of the
measurand for which the probability of a wrongconclusion that the measurand is present when it
actually is not present (‘error of the first kind’ or a ‘falsepositive error’) does not exceed a specified probability,
α.
Excerpt from Clause 3.1 of CSA N288.4-10 and N288.5-11
Definitions
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Detection limit — the level (relative to background)above which an effect can confidently be measured.
• The detection limit is the smallest value of the
measurand for which the probability of a wrong
conclusion that the measurand is not present when itactually is present (‘error of the second kind’ of a ‘falsenegative error’) does not exceed a specifiedprobability, β.
Excerpt from Clause 3.1 of CSA N288.4-10 and N288.5-11
Definitions
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Non-detect Level (LC) for Type I Error (α)Detection Limit (LD) for Type II Error (β)
Definitions
From IUPAC Recommendations 1995
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Definitions
CSA N288.4-10
Annex D, Table D.1
Formulae for non-detect (critical) level and detection limit
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The level below which quantitative results are notobtained from the measurement system or analysis
method selected is called the non-detect level.
• This level shall be defined and its derivation should be
documented.• Measurements below this level are often reported as
being “less than” some value.
Paraphrased from Clause 8.1.10 of CSA N288.5-11
CSA N288.5-11
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The treatment of results that are less than the non-detect level for the measurement shall be defined
and documented.
The results of measurements that are below the non-
detect level may be reported as “not detected” or asbeing below a “less than value”.
The values of the non-detect level should be
documented and reported.
Paraphrased from Clause 8.3 of CSA N288.4-10
CSA N288.4-10
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Quantitative numerical values should be reportedrather than “less than” some value or non-detect
level.
• The requirement for numerical values needs to be
considered within the context of laboratory capabilitiesand the proximity of the result to any applicable
benchmark value.
Paraphrased from Clause 8.1.10 of CSA N288.5-11
CSA N288.5-11
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The user is cautioned that these formula
might not be
appropriate in all
situations,
particularly in low-
level counting.
(see Strom & MacLellan,
Health Physics, 81(1),
2001).
Low Count Rates
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A note following Clause 8.1.9 of N288.5-11 warnsthat:
• …some laboratories might report an instrumentdetection level that is often much lower than the
detection level of the method (which includes anyrequired sample preparation). It is the responsibility of
the user to understand, document, and justify the
detection limit reported by any laboratory engaged to
perform analyses.
Both standards generally assume (but often do not
always explicitly state) that the detection level is the
Method Detection Level
Instrument vs. Method Detection Limits
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Unit 4b – Summary and Review
Detection Limits
Non-detect level & detection limit
Instrument Detection Limits vs. Method Detection
Limits
Learning Objective
Describe the responsibilities of the user of thestandards with regard to Detection Limits
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Unit 4c
Uncertainty
Experts Teaching from Practical Experience
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Uncertainty - a quantitative expression of error that
results from incomplete knowledge or informationabout a parameter or value.
– Measurement Uncertainty (from JCGM 200:2012 – International
Vocabulary of Metrology): non-negative parameter characterizing the
dispersion of the quantity values being attributed to a measurand,
based on the information used• Statistical uncertainty - that component of uncertainty
which arises from imprecision.
• Systematic uncertainty - that component of uncertainty
which arises from biases. Excerpt from Clause 3.1 of CSA N288.4-10
• Type A uncertainty - determined by repeated
measurement
• Type B uncertainty - determined in any other manner
Uncertainty
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N288.4: The uncertainty associated with each
measured or calculated value should be estimated. Excerpt from Clause 9.3.3.1 of CSA N288.4-10
N288.5: The uncertainty associated with results of
effluent monitoring measurements and any doseestimates derived from them should be discussed in
the report. Excerpt from Clause 9.3.3..2.1 of CSA N288.5-11
Both: The uncertainty should take into account bothsampling and measurement errors. Sampling errors
cannot always be quantified but they shall be kept to
a minimum by design of the monitoring program.
Requirements
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Introduction to Uncertainty
Two introductory texts on uncertainty:
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The Guide to Uncertainty in Measurement (GUM)
was prepared by the Joint Committee for Guides in
Metrology (JCGM)
• Originally published in 1995 (JCGM 100:1995)
• Minor revision published in 2008 (JCGM 100:2008) – Available for download from the BIPM website at
http://www.bipm.org/en/publications/guides/gum.html
The 1995 version of the GUM was adopted by ISO &
IEC as ISO/IEC Guide 98-3:2008• the 2008 revision has not yet been adopted by
ISO/IEC
Guide to Uncertainty in Measurement
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The JCGM Working Group on Uncertainty in
Measurement is preparing additional guidance on
the “Evaluation of Measurement Data”:
• Guide to the expression of uncertainty in measurement
(JCGM 100:2008)• Propagation of distributions using a Monte Carlo
method – Supplement 1 to the GUM (JCGM 101:2008)
• Extension to any number of output quantities –
Supplement 2 to the GUM (JCGM 102:2011)• Introduction to the “Guide to the expression of
uncertainty in measurement” and related documents(JCGM 104:2009)
• Other documents & supplements are in preparation
Evaluation of Measurement Data
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UKAS M3003 (Ed. 2, Jan. 2007) “The Expression of Uncertainty and Confidence in Measurement” • Provides an introduction to the subject with examples
of the application of the guidance given in the GUM
• http://www.ukas.com/library/Technical-Information/Pubs-Technical-Articles/Pubs-
List/M3003.pdf
The Eurachem/CITAC Guide (Ramsey & Ellison,
2007) “Measurement of uncertainty arising fromsampling: A guide to methods and approaches”• Provides additional guidance on the estimation of
uncertainty due to sampling and sample preparation
• http://www.eurachem.org/guides/pdf/UfS_2007.pdf
Further Guidance
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The American Society for Quality (ASQ) defines an
‘uncertainty budget’ as:
a statement of measurement uncertainty, of the
components of that measurement uncertainty, and of
their calculation and combination A National Research Council template for
Uncertainty Budgets (an Excel spreadsheet) is
available at:
www.nrc-nrc.gc.ca/obj/inms-ienm/doc/clas-clas/uncertainty_budget_template.xls
Uncertainty Budgets
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Example: Calculation of activity by liquid
scintillation counting
A = {(C-B) x exp(-λt) x R x S} / {V x T x ε x Pϒ}
Uncertainty Budgets
Symbol Symbol A Activity V Volume
C Gross Counts T Time
B Background Counts ε Efficiency
exp(-λt) Decay Factor Pϒ
Emission Probability
R Random Summing
Correction
S Self-absorption Correction
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Uncertainty Budgets
Component Value Uncertainty Distribution Divisor
Relative
Uncertainty
Gross Counts 414 20.3 Normal (1σ) 1 4.91%
Background Counts 22 4.7 Normal (1σ) 1 21.32%
Decay 1 0.10% Normal (1σ) 1 0.10%
R 1 0.30% Normal (1σ) 1 0.30%
S 1 0.30% Normal (1σ) 1 0.30%
Volume (l) 0.001 0.00002 Rectangular (half-range) √3 1.15%
Live Time (s) 600 0.001 Rectangular (half-range) √3 0.00%
Efficiency 0.34 3.50% Normal (2σ) 2 1.75%
Emission Probability 1 0.50% Normal (2σ) 2 0.25%
ACTIVITY (Bq/l) 1922 221 Normal (2σ) 11.49%
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P(x) = 1/2a, for (b –a) < x < (b+a)
0, otherwise
E(x) = Mean = bE(x2) = b2 + a2/3
σ2 = E(x2) – E(x)2 = a2/3
σ = a/√3
Rectangular Distribution
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Sampling Uncertainty
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Sampling Uncertainty should be included but it
may be difficult (or impossible) to quantify.
Possible approaches include:
• Empirical Methods
– Duplicate samples
– Multiple protocols
– Collaborative trial
– Sampling proficiency test
– Reference sampling method/target
• Modeling
– Cause & Effect Modeling
– Gy’s Sampling Theory
Sampling Uncertainty
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Sampling Uncertainty
Method
Component Estimated
Sampling Analytical
Precision Bias Precision Bias
Duplicate Samples(Single sampler using a single
sampling protocol)
Yes No Yes No *
(may be estimated by
including Certified
Reference Materials)
Multiple Protocols(Single sampler using multiple
sampling protocols)
Between protocols Yes No *
(may be estimated by
including Certified
Reference Materials)
Collaborative Trial(Multiple samplers using the same
sampling protocol)
Between samplers Yes Yes
Sampling Proficiency Test(Multiple samplers each using a
different sampling protocols)
Between samplers &
protocolsYes Yes
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“Model uncertainty” is an assessment of the degreeof confidence that a mathematical model is a
"correct“ representation of the system.
Model uncertainty includes:
• An estimate of the uncertainty due to the structure of the model (structure uncertainty); and
• An estimate of uncertainty in each of the parameters
used in the risk assessment equations (parameter
uncertainty).
Model Uncertainty
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Estimates of the uncertainties in the N288 models
are generally not available:
• Excerpt from CSA N288.1-08 Clause 4.2.9 –
“Conservatism is introduced into the current model by
selecting conservative values for food, water, soil, andair intake rates for the representative person, typically
at the 95th percentile level.”
Model Uncertainty
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Estimates of the uncertainties in the N288 models
are generally not available:
• IAEA TRS 472 (Handbook of Parameter Values for the
Prediction of Radionuclide Transfer in Terrestrial and
Freshwater Environments) – “Estimations … of uncertainty about each such value were carried out by
applying statistical analysis, where possible ... In some
cases, the values were given without a statement of
uncertainty or a range, because of the limited data
available.”
Model Uncertainty
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Estimates of the uncertainties in the N288 models
are generally not available:
• NCRP Report 164 (Uncertainties in Internal Dose
Assessment) – The dose limits that are recommended
by the International Commission on RadiologicalProtection (ICRP) for regulatory purposes are based
on the use of values of dose per unit intake that are to
be applied without any consideration of uncertainty.
Model Uncertainty
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The number of significant figures quoted in a result
shall not imply a degree of precision greater than
that warranted by the sources of uncertainty.
• Uncertainty should be rounded to one significant figure
• The least significant figure in the result shouldcorrespond to the significant figure in the uncertainty
(e.g., 1900 ± 200 Bq/l).
• More significant figures should be carried for
calculation steps than what is reported in the finalstage.
Paraphrased from Clause 9.3 of CSA N288.4-10 and CSA N288.5-11
Reporting Uncertainty
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Unit 4c – Summary and Review
Uncertainty
ISO Guide to Uncertainty in Measurement (GUM)
Uncertainty budgets
Sampling uncertainty
Reporting uncertainty
Learning Objectives
Identify the guidance on analysis of samples andreporting of results
Describe the changes in the guidance on
estimating and reporting uncertainties