TEACHING STUDENTS BASIC LAB SKILLS FOR A REGULATED ENVIRONMENT

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TEACHING STUDENTS BASIC LAB SKILLS FOR A
REGULATED ENVIRONMENT

• BIOMAN 2007
• Lisa Seidman
• Madison Area Technical College
• Madison, WI

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WHY THE BASICS?

• Needs of students
• Needs of employers

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MYTH 1

• Basics means simple, easy, obvious
• If this were true, far fewer problems in
  companies and in research labs

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BASIC MEANS

• Vital
• Essential
• Fundamental
• Primary
• Staple
• Must

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MYTH 2

• Most of this does not apply in research labs

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MYTH 3

• We all learn the basics in high school, or
  someone elses class, or by osmosis

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MYTH 4

• Basics are boring

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BASICS

• What are basics?
• Different answers, but some common themes

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HOW TO TEACH BASICS?

• Consciously
• Systematically
• model 1 way teach children music
• model 2 way grad students are taught
• Underlying principles

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THIS WORKSHOP

• Teaching basics consciously
• Systematically
• Underlying principles

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TOPICS FOR THIS WORKSHOP

• Quality
• Basic lab task making a solution
• Metrology (unifying principles)

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STORY OF FRANCES KELSEY

• Case study

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KELSEY

• Purpose
• introduce GMP
• introduce process of developing drug
• most important idea of quality
• Bureaucrat who understood quality

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• QUALITY THE BIG (BUT BRIEF) PICTURE

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WHAT IS BIOTECHNOLOGY?

• The biotechnology industry transforms scientific
  knowledge into useful products

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OVERVIEW

• Talk about product quality systems
• In broad way
• Apply ideas to the various work places we talked
  about

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QUALITY SYSTEMS

• Broad systems of regulations, standards, or
  policies that ensure the quality of the final
  product
• GMP/GLP/GCP are examples of quality systems

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WHAT IS PRODUCT QUALITY?

• What is a good product in biotechnology?
• That depends
• Consider biotech
• Research labs
• Testing labs
• Production facilities

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QUALITY PRODUCT RESEARCH LAB

• Research lab, knowledge is product
• Knowledge of nature (basic research)
• Understanding of technology (applied research,
  RD)

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QUALITY SYSTEMS IN RESEARCH LABS

• Quality system in research
• Ensure meaningful data
• has been around a long time
• It is called

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• DOING GOOD SCIENCE
• Less formalized than other quality systems
• No one book spells it out
• No laws to obey
• But it exists

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INFORMAL SYSTEM

• Consequences of poor quality product not
  life-threatening so
• Government seldom involved in monitoring research
  quality
• Oversight not generally by outside inspectors or
  auditors

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BUT THERE IS OVERSIGHT

• Oversight is by peers
• Grant review
• Publications
• Reputation

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• Compare and contrast situation in research labs
  and other work places

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PRODUCT QUALITY TESTING LAB

• Testing lab
• Information about samples
• Good product result that can be relied on when
  making decisions

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CONSEQUENCES

• A poor quality product can be life-threatening or
  have serious effects

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QUALITY SYSTEMS IN TESTING LABS

• Include most of what we call doing good science
  plus
• Specific formal requirements
• Personnel
• Equipment
• Training
• Facilities
• Documentation

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• You can find a book that spells it out for
• Clinical labs
• Forensic labs
• Environmental labs

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ENFORCEMENT TESTING LABS

• Since consequences of poor product can be
  life-threatening
• Is outside oversight
• FBI
• EPA
• Etc.

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PRODUCT QUALITY PRODUCTION FACILITY

• Make tangible items
• Quality product fulfills intended purpose
• Ex. reagent grade salt vs road salt vs table
  salt

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QUALITY SYSTEMS IN PRODUCTION FACILITIES

• Depends on nature of product
• Poor product may or may not have life-threatening
  consequences

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SO, FOR EXAMPLE

• Products for research use, not generally
  regulated
• Agricultural products are regulated in one way
• Pharmaceutical products are regulated in another

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VOLUNTARY STANDARDS

• Companies that are not regulated may choose to
  comply with a product quality system for business
  reasons

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ISO 9000

• ISO 9000
• Formal product quality system
• Extensive
• Exists in a series of books
• Companies comply voluntarily to improve the
  quality of products
• and to make more money

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OVERSIGHT ISO 9000

• Oversight by outside auditors, paid by company

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BIOTECH AND MEDICAL PRODUCTS

• Many biotech companies that make money make
  medical/pharmaceutical products
• Consequences of poor product can be
  life-threatening

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SO

• These products are highly regulated by the
  government
• But, it wasnt always this way

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• history
• CFR, handout

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HOW IS QUALITY BUILT INTO A PRODUCT?

• No single answer
• Requires
• Skilled personnel
• Well-designed and maintained facility
• Well-constructed processes
• Proper raw materials
• Documentation
• Change control
• Validation

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ENFORCEMENT

• Compliance is enforced by government
• FDA

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QUALITY IS BASIC

• Details may not be essential right now
• Idea of quality is essential

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LETS GO TO THE LABVERY BASIC LAB TASKS

• 1. Write procedure to make 100 mL of a buffer
  solution that is
• 100 mM Tris, pH 7.5
• 2 NaCl
• 10 µg/mL of proteinase K
• QC your solution by checking its conductivity
• Check the pH of a Tris buffer solution

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PROCEDURE

• For 100 mL of 100 mM Tris solution (FW 121.1)
  weigh out 1.211 g of Tris base. Dissolve in
  about 60 mL of water and adjust pH to 7.5.
• Add 2g of NaCl
• 10 µg/mL of proteinase K X 100 mL 1000 µg 1
  mg. Weigh and add to Tris.
• Dissolve, BTV, check pH

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VARIABILITY IN APPROACHES?

• Value of SOPs in ensuring consistency
• Value of communicating among various lab workers
• Documentation

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WHAT DO STUDENTS NEED TO KNOW?

• Conceptual
• Why they are making solution, context
• How to interpret recipe
• Basic calculations
• Instrumentation
• How to maintain, use, calibrate balance
• How to maintain, use, calibrate pH meter
• How to measure volume
• How to maintain, use, calibrate conductivity
  meter
• Quality control
• How to ensure that solution is what it should be
• How to document work

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TEACHING

• Concrete skills
• calculations
• using equipment
• etc.
• These are activities in the lab manual to
  systematically build these skills

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VARIABILITY

• Mike Fino

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UNDERLYING PRINCIPLES

• Quality ideas (e.g. reducing variability and
  documentation, following directionsSOPs)
• Math calculations/ideas that repeat over and over
  again
• Safety practices
• Metrology principles

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INTRODUCTION TO METROLOGYLisa SeidmanBioman 2007
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DEFINITIONS

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

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OVERVIEW

• Begin with general principles
• Next weight, volume, pH, light transmittance
  (spectrophotometry)

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WE WANT TO MAKE 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?

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

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• 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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GOOD MEASUREMENTS

• 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

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QUALITY SYSTEMS

• All laboratory quality systems are concerned with
  measurements
• All want good measurements

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NEED

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

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METROLOGY VOCABULARY

• 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

Instrumentation
Measurement itself
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UNITS OF MEASUREMENT

• 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 MADE BY AGREEMENT

• Definitions of units are made by international
  agreements, SI system
• Example, kilogram prototype in France
• K10 and K20 at NIST

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EXTERNAL AUTHORITY

• Measurements are always made in accordance with
  external authority
• Early authority 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 ARE AFFECTED 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 cm doesnt change

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STANDARDS ALSO ARE

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

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STANDARDS ALSO ARE

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

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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 balance to units of kg
• Do this in lab

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PERFORMANCE VERIFICATION IS

• Check of the performance of an instrument or
  method without adjusting it.
• Do this in lab.

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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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TRACEABILITY

• Note in this catalog example, traceable to NIST

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VOCABULARY

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

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MEASUREMENT

• What are the characteristics of good measurement?
• Accuracy
• Precision

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ACCURACY AND PRECISION ARE

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

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EXPRESS ACCURACY

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

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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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CATEGORIES OF 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 is probably really a bit light
• Average of all the values is a good estimate of
  its true weight

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RANDOM ERROR AND 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

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THERE IS ALWAYS RANDOM ERROR

• If cant see it, system isnt sensitive enough
• Less sensitive balance 10.00 g,
• 10.00 g, 10.00 g
• Versus 9.999600 g

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Mean Median Mode
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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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RELATIONSHIP RANDOM 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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• 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

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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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ANOTHER DEFINTION OF 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 AND UNCERTAINTY

• 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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UNCERTAINTY ESTIMATES

• 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. (It is often evaluated using
  Type B methods.)

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SIIGNIFICANT FIGURE CONVENTIONS

• 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 are certain of value
• Conversion factors

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ROUNDING CONVENTIONS

• Combine numbers in calculations
• Confusing
• Look up rules when they need them

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RECORDING MEASURED 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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• The specification is set at the hundredth 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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GOOD WEB SITE FOR SIGNIFICANT FIGURES

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

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THERMOMETERS

• Look at the values for the thermometers on the
  board.
• 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 they accurate?
• How could we figure out the true value for the
  temperature?

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REPEATING MEASUREMENTS

• 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.
• 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 OUR ORIGINAL 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

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ASSAYS
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SAME IDEAS APPLY

• A good assay is one can trust when making a
  decision
• Accuracy and precision
• Linearity
• Limits

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