No measurement of a physical
quantity can be entirely accurate. It is important to know,
therefore, just how much the measured value is likely to deviate
from the unknown, true, value of the quantity. The art of estimating
these deviations should probably be called uncertainty analysis,
but for historical reasons is referred to as
Error Analysis.
The brief discussions about how errors are
reported, the kinds of errors that can occur, how to estimate
random errors, and how to carry error estimates into calculated
results.
Absolute and Relative Errors
The
Absolute error in a measured
quantity is the uncertainty in the quantity and has the same
units as the quantity itself.
For example if you know a length
is
0.428 m ± 0.002 m, the
0.002 m is an
absolute error.
The
Relative error (also called the
fractional error) is obtained
by dividing the absolute error in the quantity by the quantity
itself. The relative error is usually
more significant than
the absolute error.
For example a
1 mm error in the
diameter
of a skate wheel is probably more serious than a
1 mm
in a
truck tire.
Note: that relative errors are
dimensionless.
When reporting relative errors it is usual to multiply the
fractional error by 100 and report it as a percentage.
Systematic Errors
Systematic errors arise from
a flaw in the measurement scheme which is
repeated each time
a measurement is made. If you do the same thing wrong each
time you make the measurement, your measurement will differ
systematically (that is, in the same direction each time) from
the correct result.
Some
sources of systematic error are:
Errors in the
calibration of the
measuring instruments.
Incorrect
measuring technique: For example, one might make an
incorrect scale reading because of parallax error.
Bias of the experimenter: The experimenter might consistently
read an instrument incorrectly, or might let knowledge
of the expected value of a result influence the measurements.
It is clear that systematic
errors do not average to zero if you average many measurements.
If a systematic error is discovered, a correction can be made
to the data for this error.
If you measure a voltage with a
meter that later turns out to have a 0.2 V offset, you can
correct the originally determined voltages by this amount and
eliminate the error.
One must simply sit down and think about
all of the
possible sources of error in a given measurement,
and then do small experiments to see if these sources are active.
The
goal of a good experiment is to reduce the systematic errors
to a value smaller than the random errors.
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