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Standard deviation

gitalife
Hello all,

Not being a statistician, what exactly is the standard deviation of each tip telling me.
Besides the formal definition of " a measure of how dispersed the data is in relation to the mean.",
if one of the tips has a standard deviation of .0002 (inches) and another .0005, how will that translate
to the accuracy of the real time measurements of those tips?
Is that for the deviation of the dynamic tip radius and does that differ from a qualification check which
seems to give a xyz and polar radius deviation of each tip relative to the sphere measurement location?

Thanks for any technical insight
Parents
  • Standard deviation is defined as the square root of the average, of the squared deviations of the values, subtracted from their average value.
    So, let's dwell on how that applies to a probe calibration.
    Cal sphere is 1" diameter.
    radial result of sphere at 9 hits are as follows:
    0.5005
    0.5009
    0.5010
    0.4997
    0.5002
    0.4990
    0.5004
    0.5000
    0.4996
    This equates to an average of 0.500144
    With measurement uncertainty (max-min) of 0.002"
    Standard deviation of those hits is just 0.000608"

    So your measurement uncertainty in this example is 3.3x worse than the standard deviation value.
    This can and will get significantly worse if the outlier points are distributed about a larger range.
Reply
  • Standard deviation is defined as the square root of the average, of the squared deviations of the values, subtracted from their average value.
    So, let's dwell on how that applies to a probe calibration.
    Cal sphere is 1" diameter.
    radial result of sphere at 9 hits are as follows:
    0.5005
    0.5009
    0.5010
    0.4997
    0.5002
    0.4990
    0.5004
    0.5000
    0.4996
    This equates to an average of 0.500144
    With measurement uncertainty (max-min) of 0.002"
    Standard deviation of those hits is just 0.000608"

    So your measurement uncertainty in this example is 3.3x worse than the standard deviation value.
    This can and will get significantly worse if the outlier points are distributed about a larger range.
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