INTRODUCTION TO METROLOGY

1
INTRODUCTION TO METROLOGY
2
Definitions

• Metrology is the study of measurements
• Measurements are quantitative observations
  numerical descriptions

3
Overview

• This longer lecture explores general principles
  of metrology
• Next 3 shorter lectures apply principles to
  specific measurements weight, volume, pH
• Later will talk about measuring light
  transmittance (spectrophotometry)

4
We Want ToMake Good Measurements

• Making measurements is woven throughout daily
  life in a lab.
• Often take measurements for granted, but
  measurements must be good.
• What is a good measurement?

5
Example

• A man weighs himself in the morning on his
  bathroom scale, 172 pounds.
• Later, he weighs himself at the gym,173 pounds.

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Questions

• How much does he really weigh?

7

• Do you trust one or other scale? Which one?
  Could both be wrong? Do you think he actually
  gained a pound?

8

• Are these good measurements?

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Not Sure

• We are not exactly certain of the mans true
  weight because
• Maybe his weight really did change always
  sample issues
• Maybe one or both scales are wrong always
  instrument issues

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Do We Really Care?

• Do you care if he really gained a pound?
• How many think give or take a pound is OK?

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Another Example

• Suppose a premature baby is weighed. The weight
  is recorded as 5 pounds 3 ounces and the baby is
  sent home.
• Do we care if the scale is off by a pound?

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GoodMeasurements

• A good measurement is one that can be trusted
  when making decisions.
• We just made judgments about scales.
• We make this type of judgment routinely.

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In The Lab

• Anyone who works in a lab makes judgments about
  whether measurements are good enough
• But often the judgments are made subconsciously
• Differently by different people
• Want to make decisions
• Conscious
• Consistent

14
Quality Systems

• All laboratory quality systems are concerned with
  measurements
• All want good measurements that can be
  trusted
• Measurements should be made consciously and
  consistently

15
Need

• Awareness of issues so can make good
  measurements.
• Language to discuss measurements.
• Tools to evaluate measurements.

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MetrologyVocabulary

• Very precise science with imprecise vocabulary
• (word precise has several precise meanings that
  are, without uncertainty, different)
• Words have multiple meanings, but specific
  meanings

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Vocabulary

• Units of measurement
• Standards
• Calibration
• Traceability
• Tolerance

• Accuracy
• Precision
• Errors
• Uncertainty

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Units OfMeasurement

• Units define measurements
• Example, gram is the unit for mass
• What is the mass of a gram? How do we know?

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Definitions MadeBy Agreement

• Definitions of units are made by international
  agreements, SI system
• Example, kilogram prototype in France
• Ultimate definition of a kg
• K10 and K20 are standards at NIST that are
  compared regularly to the prototype in France

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External Authority

• Measurements are always made in accordance with
  external authority
• Early authority for length was Pharaohs arm
  length

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• A standard is an external authority
• Also, standard is a physical embodiment of a unit

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Standards Are

• Physical objects, the properties of which are
  known with sufficient accuracy to be used to
  evaluate other items.

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Standards AreAffected By The Environment

• Units are unaffected by the environment, but
  standards are
• Example, Pharaohs arm length might change
• Example, a ruler is a physical embodiment of
  centimeters
• Can change with temperature
• But the unit cm doesnt change

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StandardsAlso Are

• In chemical and biological assays, substances or
  solutions used to establish the response of an
  instrument or assay method to an analyst
• See these in spectrophotometry labs

25
StandardsAlso Are

• Documents established by consensus and approved
  by a recognized body that establish rules to make
  a process consistent
• Example ISO 9000 is a Standard
• ASTM standard method for calibrating a
  micropipettor is the recommended procedure

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Calibration Is

• Bringing a measuring system into accordance with
  external authority, using standards
• For example, calibrating a balance
• Use standards that have known masses
• Relate response of your balance to units of kg
• Will do this in lab

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PerformanceVerification Is

• Check of the performance of an instrument or
  method without adjusting it.

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Tolerance Is

• Amount of error that is allowed in the
  calibration of a particular item. National and
  international standards specify tolerances.

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Example

• Standards for balance calibration can have slight
  variation from true value
• Highest quality 100 g standards have a tolerance
  of 2.5 mg
• 99.99975-100.00025 g
• Leads to uncertainty in all weight measurements

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Traceability Is

• The chain of calibrations, genealogy, that
  establishes the value of a standard or
  measurement
• In the U.S. traceability for most physical and
  some chemical standards goes back to NIST

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From Basic Laboratory Methods for Biotechnology
Textbook and Laboratory Reference, Seidman and
Moore, 2000
32
Traceability

• Note in this catalog example, traceable to NIST

33
Vocabulary

• Standards
• Calibration
• Traceability
• Tolerance
• Play with these ideas in labs

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Accuracy AndPrecision Are

• Accuracy is how close an individual value is to
  the true or accepted value
• Precision is the consistency of a series of
  measurements

From Basic Laboratory Methods for Biotechnology
Textbook and Laboratory Reference, Seidman and
Moore, 2000
35
Express Accuracy

• error True value measured value X 100
• True value
• Will calculate this in volume lab

36
Express Precision

• Standard deviation (p. 187-190)
• Expression of variability
• Take the mean (average)
• Calculate how much each measurement deviates from
  mean
• Take an average of the deviation, so it is the
  average deviation from the mean
• Try this in the volume lab

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Error Is

• Error is responsible for the difference between a
  measured value and the true value

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CategoriesOf Errors

• Three types of error
• Gross
• Random
• Systematic

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Gross Error

• Blunders

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Random Error

• In U.S., weigh particular 10 g standard every
  day. They see
• 9.999590 g, 9.999601 g, 9.999592 g .
• What do you think about this?

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Random Error

• Variability
• No one knows why
• They correct for humidity, barometric pressure,
  temperature
• Error that cannot be eliminated. Called random
  error

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Random Error

• Do you think that repeating the measurement over
  and over would allow us to be more certain of the
  true weight of this standard?

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Random Error

• Yes, because in the presence of only random
  error, the mean is more likely to be correct if
  repeat the measurement many times
• Standard in this example is probably really a bit
  light
• Average of all the values is a good estimate of
  its true weight

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Random ErrorAnd Accuracy

• In presence of only random error, average value
  will tend to be correct
• With only one or a few measurements, may or may
  not be accurate see picture b in next slide

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From Basic Laboratory Methods for Biotechnology
Textbook and Laboratory Reference, Seidman and
Moore, 2000
46
MeasurementVariability
Measurement value
FIRST MEASUREMENT OF SAME THING
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MeasurementVariability
Measurement value
THREE MEASUREMENTS OF THE SAME THING
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MeasurementVariability
Measurement value
SEVEN MEASUREMENTS OF THE SAME THING
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MeasurementVariability
Measurement value
MORE MEASUREMENTS OF THE SAME THING
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MeasurementVariability
Mean Median Mode
Repeated measurements of the same thing tend to
be Normally distributed
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There Is AlwaysRandom Error

• If cant see it, system isnt sensitive enough
• Less sensitive balance might read
• 10.00 g, 10.00 g, 10.00 g
• Versus 9.999590 g, 9.999601 g, 9.999592 g .
• See the variability with the sensitive balance

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So

• Can we ever be positive of true weight of that
  standard?
• No
• There is uncertainty in every weight measurement

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RelationshipRandom Error And Precision

• Random error
• Leads to a loss of precision

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Systematic Error

• Defined as measurements that are consistently too
  high or too low, bias
• Many causes, contaminated solutions,
  malfunctioning instruments, temperature
  fluctuations, etc., etc.

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Systematic Error

• Technician controls sources of systematic error
  and should try to eliminate them, if possible
• Temperature effects
• Humidity effects
• Calibration of instruments
• Etc.

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Systematic Error

• In the presence of systematic error, does it help
  to repeat measurements?

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Systematic Error

• Systematic error
• Does impact accuracy
• Repeating measurements with systematic error does
  not improve the accuracy of the measurements
  because the same error is always there

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Without Systematic Error
MeanMedianMode true value
With Systematic Error
True Value
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Another DefintionOf Error Is

• Error is the difference between the measured
  value and the true value due to any cause
• Absolute error True value - measured value
• Percent error is
• True value - measured value (100 )
• True value

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Errors AndUncertainty

• Errors lead to uncertainty in measurements
• Can never know the exact, true value for any
  measurement.
• Idea of a true value is abstract never
  knowable.
• In practice, get close enough

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Uncertainty Is

• Estimate of the inaccuracy of a measurement that
  includes both the random and systematic
  components.

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Uncertainty Also Is

• An estimate of the range within which the true
  value for a measurement lies, with a given
  probability level.

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Uncertainty

• Not surprisingly, it is difficult to state, with
  certainty, how much uncertainty there is in a
  measurement value.
• But that doesnt keep metrologists from trying

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Metrologists

• Metrologists try to figure out all the possible
  sources of uncertainty and estimate their
  magnitude
• One or another factor may be more significant.
  For example, when measuring very short lengths
  with micrometers, care a lot about repeatability.
  But, with measurements of longer lengths,
  temperature effects are far more important

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Report Values

• Metrologists come up with a value for uncertainty
• You may see this in catalogues or specifications
• Example
• measured value an estimate of uncertainty

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UncertaintyEstimates

• Details are not important to us now
• But principle is any measurement, need to know
  where the important sources of error might be

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Significant Figures

• One cause of uncertainty in all measurements is
  that the value for the measurement can only read
  to a certain number of places
• This type of uncertainty. It is called
  resolution error.

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Significant FigureConventions

• Significant figure conventions are used to record
  the values from measurements
• Expression of uncertainty
• Also apply to very large counted values
• Do not apply to exact values
• Counts where you are certain of value
• Conversion factors

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Which RulerGives the Length of the Arrow With
More Certainty?
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RoundingConventions

• Use when combine numbers in calculations
• Can be confusing
• Look up rules when you need them

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RecordingMeasured Values

• Record measured values (or large counts) with
  correct number of significant figures
• Dont add extra zeros dont drop ones that are
  significant
• With digital reading, record exactly what it
  says assume the last value is estimated
• With analog values, record all measured values
  plus one that is estimated
• Discussed in Laboratory Exercise 1

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Rounding

• A Biotechnology company specifies that the level
  of RNA impurities in a certain product must be
  less than or equal to 0.02. If the level of RNA
  in a particular lot is 0.024, does that lot meet
  the specifications?

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Rounding

• The specification is set at thehundredth decimal
  place. Therefore, the result is rounded to that
  place when it is reported. The result rounded is
  therefore 0.02, and it meets the specification.

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Rounding

• Look at all the problems for chapter 13.

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Good Web Site For Significant Figures

• http//antoine.frostburg.edu/cgi-bin/senese/tutori
  als/sigfig/index.cgi

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Match these descriptions with the 4
distributions in the figure

• Good precision, poor accuracy
• Good accuracy, poor precision
• Good accuracy, good precision
• Poor accuracy, poor precision

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Thermometers

• Each person should read the value from any of the
  six thermometers placed in the beaker in front of
  the class
• Each person should record on the board the
  temperature of the water in the beaker, based on
  his/her own measurement
• Do not consult other people

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Thermometers

• Look at the values for the thermometers on the
  board.
• Do the numbers of figures recorded agree?
• Significant figure conventions can guide us in
  how to record the value that we read off any
  measuring instrument.
• With these thermometers, correct number of sig
  figs is _______.

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Thermometers

• Were the thermometers accurate?
• Explain your answer
• How could we figure out the true value for the
  temperature?

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RepeatingMeasurements

• Would repeating measurements with these
  thermometers, assuming we did not calibrate them,
  improve our ability to trust them?
• Is their error an example of random or systematic
  error?

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Calibration

• Calibration of the thermometers could lead to
  increased accuracy
• This is a type of systematic error
• In the presence of systematic error, repeating
  the measurement will not improve its accuracy

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Tolerance

• Here is a catalog description of mercury
  thermometers.
• Based on this description, are these thermometers
  out of the range for which their tolerance is
  specified?

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Precision

• Were they precise? How could precision be
  measured?
• Would calibration help to make them more precise?

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Calibration

• Calibration would probably not improve their
  precision

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Return To OurOriginal Type Of Question

• Are our temperature measurements good
  measurements?
• How do you make that judgment?
• Can we trust them?

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Thermometers Good Enough?

• Are times that we need to be very close in
  temperature measurements. For example PCR is
  fairly picky.
• Other times we can be pretty far off and process
  will still work.

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Explore Some Of These Ideas

• In lab
• Calibrate instruments
• Use standards
• Check performance of pipettors
• Record measurement values
• Calculate per cent errors
• Calculate repeatability