Recep
In classical statistics, your data have to satisfy the following two basic
assumptions:
1. Identically Distributed (ID): This assumption implies that you can
repeat the experiment infinite number of times UNDER the same
condition(s).
2. Independent (I): The result of each experiment will be independent from
previous and next trials.
Sometimes, statisticians refer to these two assumptions as IID assumptions.
Unfortunately, none of earth science data satisfies these two assumptions.
One cannot measure rainfall at a single spatial point several times under
the same condition(s). Even though, one may conceptualize the whole domain
to be generated by a random function (i.e., a collection of random
variables), one cannot characterize this random function as only ONE
realization of this RF is available. As such, one has to imposed various
assumptions such as isotropy, stationarity and ergodicity to be able to
exit this dead end. You may want to refer to archieve (previous postings)
to get more info. regarding your original enquiry. In short, replication
has no meaning in earth science data because you cannot keep the
situation for two experiments similar.
Hope this helps.
Thanks
Abedini
On Mon, 20 Feb 2006, Recep kantarci wrote:
Dear list members
My question is about the practice of replications.
As you know, in order to avoid measurement/analysis errors, one may wish to
replicate his/her measurement at the same spatial point in the field or his/her
analysis for the same sample in the laboratory many times. For example, it is a
usual practice to do three times, and discard the extreme one, if exists, then
take the average of the rest.
How can we use or treat these kind of replicated data for the same spatial
point within framework of geostatistical concepts?
Reagrds
Recep
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