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62 Cards in this Set

  • Front
  • Back

Descriptive Statistics

Summarize a data set into a few numbers that represent or describe a group of numbers.


Inferential Statistics

Compare information from one set of data to another set of data to make a comparison or inference about the data

T-test or linear regression


the entire group of items that could possibly be measured

Represented by N


A subset from the population. Should be randomly selected to accurately represent the population

Represented by n


the characteristic you want to measure


the values you obtain from a sample population

A single value is call a datum

data set

set of variable connected


a planned activity whose result yield a data set


a numerical value that summarizes one aspect of the data from a population


a numerical value that summarizes one aspect of the data from a sample


Yes/No values


less than greater than


an actual number


the measurement closest to the true value

Inaccuracy: lack of closeness to the true value


the repeatability of the process how reproducible are your values

Inaccuracy and Variability

Inaccuracy must be detectable and is accepted within it's defined limits

variability: is expected and accepted within defined limits


We must decide what is acceptable

In the lab we test QC and use QC rules to do this

Qualitative Data

Gives a general impression:

  • Pie charts
    • Bar graphs: vertical or horizontal categorize data, show a trend

Ways to show quantitative data

  • Data columns
  • Dot chart or line graph
  • Frequency distribution
    • Histogram- smoothing out frequency trend

Frequency distribution

  • Data can be graphed by how often they appear or occur


  • The frequency distribution of repeated measurement should have a bell shaped most should fall within the center

Meanm Median, Mode

In a perfect distribution they should be the same


add all the numbers divided by the number of data


Middle number

Even set of numbers divide by 2

Odd set of number (n+1)/2

Not effected by extreme values


the number, or numbers, that occur the most frequently

Some data is bimodal, sometimes there is no modal number

not effected by extreme values

Measures of variability- Gaussian distribution

  • If data is perfectly distributed, a histogram of the data appears as a bell shape
  • Central tendencies: mean, median, mode
    • Variability: variance, SD, % coefficient of variation

Measures of variability-Standard deviation and variance

Coefficient of Variation

The smaller the CV the better the precision

The smaller the CV the better the precision

Shift from the mean

systematic error

Can fix

5 stages of QC

  1. Preparation- what QC should I use
  2. Planning- what can go wrong
  3. Prevention- minimize errors
  4. Practice- train employees, compliance
  5. Performance- testing and eval

Why test QC material

  • So we know our testing procedure is accurate and precise
  • Ensure results are reliable, accurate, correct, trustworthy
  • Is the instrument always right
    • Regulatory issue: CLIA '88

QC material- Selection and USe

  • Anything that simulates a patient sample, with known analyte values
  • QCs matrix must be similar to the patient specimen
    • Caution: QC material should be handled as if it contain bio-hazardous material

Assayed control

QC material that has been repeatedly analyzed by the manufacturer:

Includes means and SDs

Statistical data is supplied with the QC material


Unassayed controls

have not been tested for concentration or variability

For all controlled material

The mean and the SD must be verified or established in your lab before use in your QC program

QC with different lot # may have different analyte concentrations

New lot #

New mean and SD must be established

Minimum QC

two levels per day for each analyte
Exception: more than 2 levels may be required, more often than once per day (blood gas)

Frequency of QC testing

Instrument Manufacturer sets

Experience dictates frequency of QC

Criticality of the analyte dictates frequency

QC levels

Normal: Ref range

Below: the normal/ref range

Above: the normal/ref range

QC level 1

usually in the "normal/ref range"

QC level 2

usually in the abnormal range ( some high some low)

QC level 3

Low abnormal, normal, and high abnormal- used in hematology for drug testing

Evaluation of QC data

Based on Gaussian Curve:

  • Test QC material and evaluation QC data
    • QC rules are based on a normal Gaussian distribution of QC data: Mean=Median=Mode, presence of random error give the curve its shape

Levey-Jennings Chart

  • Daily QC data is recorded on a Levey-Jennings chart
  • L-J chart plots QC data over time
    • First the mean and SD are determined by collecting a minimum of 20 QC data for an analyte
      • The Gaussian distribution is calculated using the means and multiples of the SD

Creating a Levey-Jennings Chart

  • turn gaussian curve on its side
  • extend X and SD lines to the right
  • Label the X-axis (days of the month) and Label the y-axis (units of concentration)
    • Label chart with: qc level, lot #, data, analyte name

Analytic Errors

  • Proper distribution of QC data: 95% of the data within 2SD
  • Random errors: creates curve
    • Systematic error: affects all results predictably (shift, trend)
      • RE+SE=TE

Random Errors

  • Imprecision- occurs by chance
  • Related to precision/imprecision
  • Random error defines the SD and %CV
    • Routine maintenance and calibration will not reduce the occurrence of random QC erro

Systemic Error

  • Represents a real-time shift or bias
  • Affects all samples
    • affects accuracy
      • Routine maintenance and calibration can minimize and correct systemic error
      • Shift and trend

Gaussian distribution

  • 1SD: 68.2%
  • 2SD: 95.5%
  • 3SD: 99.7


  • A bias exist that causes all data to be higher or lower than is correct
    • Abrupt change- away from the mean; is immediately measurable


  • The bias that exists becomes greater and greater, but slowly over time
    • Slow progression: data moving slowly away from the mean, either positive or negative direction; it takes time for its effect to be measurable


The QC data shifts/move abruptly away from the mean for number of days


QC data either decreases or increases consistently over a period of 7 days

Westgard QC multi-rules

IDs the presence of:

  • True QC failures: true positive
  • To reject false alarms: false positive
    • Direct the technologist to the correct response in resolving the Failed QC

One (1) QC warning rule

gets your attention

Five (5) QC failure rules

used to reject qc data: 5 rules are applied if 2 QC levels are used, other rules exist if 3 qc levels are used

Why repeat QC with a 13s QC error

  • 13s error is RE
  • Probability that the number is a true value
  • If this number is truly a random error, where will the next fall
    • Repeat QC is easiest and is recommended

Why troubleshoot QC with a 22s error

  • Repeating is a waste of time and money
  • What is the probability that these 2 numbers represent the true value of QC
    • Probability proves these two do not represent the true value. A bias exists

Troubleshoot QC Systemic error

  • Inspect L-J control chart and failed QC to determine type of error
  • Relate type of error to possible cause
  • consider factors in common w/ QC and on multi-test systems
  • relate causes to recent change
    • verify the solution and document the remedy

41s error

  • Four concurrent occurrences >1SD
  • systemic error detector
  • QC has failed
    • Repeating QC is a waste of time; troubleshoot

10x failure rule

  • 10 consecutive control values lie on the same side of the mean
  • systematic error detector
    • QC has failed; troubleshoot

Look at QC rules when

QC violates the 1-2s warning rule, look at QC data continually

Common mistakes in use of QC

  • No reviewing all the QC results and QC rules before testing patient results
  • Don't just repeat and repeat the same failed QC material hoping for a better result. Have a plan
    • Don't mix up QC rules with real results