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Statistical Testing
Manual MOVE3
Suppose that the datasnooping on the observations in a network has
resulted in a (limited) number of rejections. It is assumed that the rejections
are not caused by mathematical model errors, and that obvious errors such
as typing mistakes have been fixed. This leaves a number of options:
• Remove the corresponding observation.
This is a valid but rather abrupt way of handling rejections. Remember
that the removal of observations decreases the redundancy, and therefore
influences precision and reliability.
• Remeasure the corresponding observation.
Remeasuring observations is an obvious but often expensive way to
eliminate rejections, especially when the fieldwork is already completed. It
is therefore recommended to process the data on the site.
• Increase the standard deviation of the corresponding observations.
Increasing the standard deviation of an observation will always work,
meaning that it will always result in lowering the F-, W-, T-test values.
However, one should keep in mind that the goal is not to get all tests
accepted, but rather to detect blunders or model errors.
• Ignore the rejections.
This option is obviously very risky and is only applicable in case the W-test
values just exceed the critical value. It is useful to look at the estimate of
the error involved with the rejection, and to see whether it is acceptable or
not. We also have to remember that, depending on the level of
significance, there is always a probability of rejecting a valid observation.
It must be stressed that an observation must never be edited in order to
make it better fit in with the other observations of the network, unless there is
clear evidence of the source of the errors, e.g. a typing error.
4.4.6. Estimated Errors
The size of the error responsible for the rejection of an observation or known
coordinate is estimated by MOVE3. This so-called estimated error is a useful
tool, but should be handled with care:
• Only the estimated error associated with the largest W-test or T-test
should be considered.
• As far as the W-test is concerned, the estimated error is based on the
conventional alternative hypothesis that just one observation or known
coordinate contains an error. Consequently if more errors are present in
the network the result of the estimation could be meaningless, unless
errors have been made (geographically) far apart.
• As far as the T-test is concerned, the estimated error is based on the
hypothesis that just one GPS baseline or known station contains an error.
Consequently if more errors are present in the network the result of the
estimation could be meaningless, unless errors have been made
(geographically) far apart.
• Known coordinates are tested by the W-test and T-test in a constrained
network adjustment. The test results and estimated errors are only
meaningful when observational errors have been filtered out in the
foregoing free network adjustment and testing.
@ Grontmij Geogroep
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