Estimating the errors inherent in the kind of measurements used by Maciejewski and his co-authors is a tough thing to do, Bean says; if
the actual errors in the data were larger than the researchers had accounted for, the variations in the observed transit times could vanish.
Not exact matches
Since Mr. Brundige cited statistics
in his report to the town board it would be silly not to conclude this letter with statistics based purely on logic: Since the
error of machine - collected
data at the airport versus
actual human
data collection is 10 times what Mr. Brundige reported for a specific period of time, then the town board must correct all
data in Mr. Brundige's report by multiplying the figures by 10.
The good news is that most of the
errors occur when researchers interpret the
data for submission to a journal, not during
actual experiments
in the lab.
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Assumes no responsibility for
errors and / or omissions
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data and makes no representations express or implied to any
actual or prospective purchaser of the vehicle as to the condition of the vehicle, vehicle specifications, ownership, vehicle history, equipment / accessories, price or warranties.
The publisher assumes no responsibility for
errors and / or omissions
in this
data the compilation of this
data and makes no representations express or implied to any
actual or prospective purchaser of the vehicle as to the condition of the vehicle, vehicle specifications, ownership, vehicle history, equipment / accessories, price or warrant
But they'd have to be damned clear about how they're calculating unit sales, would have to aggregate sufficient
data so that they had an
error estimate
in their calculation, would have to poll for lengthy periods of time, and would have to test their results against
actual data to see how the model (and this is a model of earnings, not
data about earnings) corresponds with reality.
So please give an example where measurements of any given phenomenon were taken, with a potential
error range of the equivalent of + / - 5 % that you stipulated
in the initial measurements, where that poor initial
data was processed using statistics, and provided an «
actual average measurement within + / -.03 %, that was then verified against later, with subsequent more accurate measurements.
Given the absence of any physical evidence (such as
actual testing of a float) that the floats are
in error, I'm perfectly willing to accept their
data — which shows the oceans are losing heat at a rate ~ 50 times as fast as they supposedly gained it over the last century.