Hi:
If we can neglect the cost of collecting the samples of a
statistical experiment, is it true that the more sample number the
better, e.g., higher statistical power?
Thanks,
Cosine wrote:
Hi:
If we can neglect the cost of collecting the samples of a
statistical experiment, is it true that the more sample number the
better, e.g., higher statistical power?
Thanks,
The answer is Yes, No, and Maybe.
If you start with a design that has absolutely no power to detect what
you are lookng for, then increasing the number of samples won't affect
that. Similarly, if you have a complicated experiment looking at
several possible effects simultaneously then there may be ways of
increasing the overall sample-size that only affects the power for some
of these effects.
Perhaps more practically, with "an experiment" there is the underlying
idea of having to maintain a constant set of conditions in which you
are not interested or know nothing about. You can imagine that taking
many samples may take a long time, during which un-monitored conditions
may wander off.
One should also remember the question of quality-control of the data
and the effect of the number of samples on how well this can be done.
If you end up with more invalid data in a dataset, then the power can
go down
But "Yes", if you have a carefully thought-out and well-orgainsed >experimental set-up.
Just thought of another question. Would it be true that taking a
large sample size N makes those rare events more likely happen?
Consequently, this would make us more likely to reject H0 when
actually we should accept it?
After all, in doing a hypothesis test, we assign H0, Ha, and alpha.
Then we take samples and get a p-value. If this p-value is less than
alpha/2, we say that the result of this sample reflects the happening
of an event being rarer than the extremity. Therefore we reject the
H0. But isn't it true that with a large sample, more likely those
rare events would happen?
Just thought of another question. Would it be true that taking
a large sample size N makes those rare events more likely happen? >Consequently, this would make us more likely to reject H0 when
actually we should accept it?
After all, in doing a hypothesis test, we assign H0, Ha, and alpha.
Then we take samples and get a p-value. If this p-value is less
than alpha/2, we say that the result of this sample reflects the
happening of an event being rarer than the extremity.
Therefore we reject the H0. But isn't it true that with a large sample,
more likely those rare events would happen?
Cosine wrote:
Hi:
If we can neglect the cost of collecting the samples of a
statistical experiment, is it true that the more sample number the
better, e.g., higher statistical power?
Thanks,
The answer is Yes, No, and Maybe.
If you start with a design that has absolutely no power to detect what
you are lookng for, then increasing the number of samples won't affect
that. Similarly, if you have a complicated experiment looking at
several possible effects simultaneously then there may be ways of
increasing the overall sample-size that only affects the power for some
of these effects.
Perhaps more practically, with "an experiment" there is the underlying
idea of having to maintain a constant set of conditions in which you
are not interested or know nothing about. You can imagine that taking
many samples may take a long time, during which un-monitored conditions
may wander off.
One should also remember the question of quality-control of the data
and the effect of the number of samples on how well this can be done.
If you end up with more invalid data in a dataset, then the power can
go down.
But "Yes", if you have a carefully thought-out and well-orgainsed experimental set-up.
Hi:
If we can neglect the cost of collecting the samples of a statistical experiment, is it true that the more sample number the better, e.g., higher statistical power?
Thanks,
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