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ATMS 320 – Fall 2019
Error
Dr. Christopher M. Godfrey
University of North Carolina at Asheville
Train wreck at Montparnasse, France, 1895
ATMS 320 – Fall 2019
Gaussian Distribution
Standard
Deviation ()
Mean ()
ATMS 320 – Fall 2019
Sources of Random Errors
 Sudden or uncontrollable changes within the
measuring environment that are significant
enough to affect the measurement
 Electronic or electrical noise
 Random, careless errors of the operator
ATMS 320 – Fall 2019
1679.9
1680
1680.1
1680.2
1680.3
1680.4
1680.5
1000 1050 1100 1150 1200 1250 1300 1350 1400
Time (minutes after midnight 1 June 2002)
Frequency(Hz)
Frequency is proportional to weight of water in the bucket
Noise in the Geonor vibrating wire rain gauge
One-minute Observations
ATMS 320 – Fall 2019 ATMS 320 – Fall 2019
2
ATMS 320 – Fall 2019
Bias vs. Precision
 Biased
 Precise
 Unbiased
 Imprecise
 Unbiased
 Precise
ATMS 320 – Fall 2019
Bias vs. Precision
 Biased
 Precise
 Unbiased
 Imprecise
 Unbiased
 Precise
ATMS 320 – Fall 2019
Accuracy Measures
Root-mean squared error
 Sensitive to large errors
 Same physical dimensions as calibrated
output


N
i
ie
N
RMSE
1
21
ATMS 320 – Fall 2019
Accuracy Measures
Root-sum square
 Assesses total error from multiple
independent, measurable sources
 Often less than simple sum of the error
components


N
i
ieRSS
1
2
RSS
ATMS 320 – Fall 2019
Multiple Sources of Error (Geonor)
 Drift (evaporation)
 Temperature dependence (a secondary input)
 Random noise
57.5
58.0
58.5
59.0
59.5
60.0
60.5
61.0
61.5
62.0
7/16/2001 7/18/2001 7/20/2001 7/22/2001 7/24/2001 7/26/2001 7/28/2001
Time (UTC)
Accumulation(mm)
ATMS 320 – Fall 2019
Significant Figures
 An example:
 Thermometer has imprecision of 0.12 K
 Thermometer reads 273.781 K
 Correct value likely lies within the range 273.661 K
and 273.901 K (or 273.781 ± 0.12 K)
 Last two digits are not significant (they vary wildly!)
 Tenths digit is uncertain (between 7 and 9 when
rounded), so we keep four significant digits, including the
uncertain one
 Observation should be reported as 273.8 ± 0.1 K
3
ATMS 320 – Fall 2019
Significant Figures
 Rules for applying significant digits:
 Nonzero digits are significant
 Zeros between nonzero digits are significant
 Zeros to the left of the first nonzero digit in a
number are not significant
 Zeros to the right of the decimal point are
significant
 Some ambiguity when a number ends in zeros
that are not to the right of the decimal point
 Fix this problem by using scientific notation
ATMS 320 – Fall 2019
Significant Figures
 Rules for mathematical operations processed with
calculators or computers
 Store all intermediate results to the precision of the
computer or calculator (don’t confuse this with the precision
of the instrument!)
 Round the final result to the appropriate precision
 Accuracy of the final result is limited by the least accurate
measurement
 Mathematical operations do not improve precision of result,
but careless handling of intermediate results can decrease
the final precision
 Do not round or truncate sums when calculating transfer
coefficients using the method of least squares!

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Error

  • 1. 1 ATMS 320 – Fall 2019 Error Dr. Christopher M. Godfrey University of North Carolina at Asheville Train wreck at Montparnasse, France, 1895 ATMS 320 – Fall 2019 Gaussian Distribution Standard Deviation () Mean () ATMS 320 – Fall 2019 Sources of Random Errors  Sudden or uncontrollable changes within the measuring environment that are significant enough to affect the measurement  Electronic or electrical noise  Random, careless errors of the operator ATMS 320 – Fall 2019 1679.9 1680 1680.1 1680.2 1680.3 1680.4 1680.5 1000 1050 1100 1150 1200 1250 1300 1350 1400 Time (minutes after midnight 1 June 2002) Frequency(Hz) Frequency is proportional to weight of water in the bucket Noise in the Geonor vibrating wire rain gauge One-minute Observations ATMS 320 – Fall 2019 ATMS 320 – Fall 2019
  • 2. 2 ATMS 320 – Fall 2019 Bias vs. Precision  Biased  Precise  Unbiased  Imprecise  Unbiased  Precise ATMS 320 – Fall 2019 Bias vs. Precision  Biased  Precise  Unbiased  Imprecise  Unbiased  Precise ATMS 320 – Fall 2019 Accuracy Measures Root-mean squared error  Sensitive to large errors  Same physical dimensions as calibrated output   N i ie N RMSE 1 21 ATMS 320 – Fall 2019 Accuracy Measures Root-sum square  Assesses total error from multiple independent, measurable sources  Often less than simple sum of the error components   N i ieRSS 1 2 RSS ATMS 320 – Fall 2019 Multiple Sources of Error (Geonor)  Drift (evaporation)  Temperature dependence (a secondary input)  Random noise 57.5 58.0 58.5 59.0 59.5 60.0 60.5 61.0 61.5 62.0 7/16/2001 7/18/2001 7/20/2001 7/22/2001 7/24/2001 7/26/2001 7/28/2001 Time (UTC) Accumulation(mm) ATMS 320 – Fall 2019 Significant Figures  An example:  Thermometer has imprecision of 0.12 K  Thermometer reads 273.781 K  Correct value likely lies within the range 273.661 K and 273.901 K (or 273.781 ± 0.12 K)  Last two digits are not significant (they vary wildly!)  Tenths digit is uncertain (between 7 and 9 when rounded), so we keep four significant digits, including the uncertain one  Observation should be reported as 273.8 ± 0.1 K
  • 3. 3 ATMS 320 – Fall 2019 Significant Figures  Rules for applying significant digits:  Nonzero digits are significant  Zeros between nonzero digits are significant  Zeros to the left of the first nonzero digit in a number are not significant  Zeros to the right of the decimal point are significant  Some ambiguity when a number ends in zeros that are not to the right of the decimal point  Fix this problem by using scientific notation ATMS 320 – Fall 2019 Significant Figures  Rules for mathematical operations processed with calculators or computers  Store all intermediate results to the precision of the computer or calculator (don’t confuse this with the precision of the instrument!)  Round the final result to the appropriate precision  Accuracy of the final result is limited by the least accurate measurement  Mathematical operations do not improve precision of result, but careless handling of intermediate results can decrease the final precision  Do not round or truncate sums when calculating transfer coefficients using the method of least squares!