Your first question is missing two choices: axiomatic and subjective.
Neither of the answers proffered in response to your second question
deal with the sums of variables which are almost always zero but take a
non-zero value with a small probability.
Phillip Good
-------- Original Message --------
Subject: [SPAM] Two statistics multiple-choice questions
From: Burke Johnson <[log in to unmask]>
Date: Thu, June 17, 2010 8:26 am
To: [log in to unmask]
Hello Colleagues,
I need your advice on two unrelated questions, as follows:
First Question:
Would you say that traditional hypothesis testing (based on alpha levels
set by a researcher in combination with empirical p values or use of
tabled critical values) and traditional confidence intervals (with
confidence placed in the long term process) is based on
(a) the classical view/interpretation of probability or
(b) the frequentist view/interpretation of probability?
Second Question:
If you had to use a non mathematical (intuitive) definition of the CLT,
which of the following would you prefer? Note that only four
(underlined) words differentiate the two definitions:
Definition-(a) Central Limit Theorem A statistical proposition to the
effect that the larger a sample size the more closely a *sampling
distribution of a statistic will approach a *normal distribution. This
is true even if the population from which the sample is drawn is not
normally distributed. A sample size of 30 or more will usually result in
a sampling distribution for the mean that is very close to a normal
distribution. The central limit theorem explains why *sampling error is
smaller with a large sample than with a small sample and why we can use
the *normal distribution to study a wide variety of statistical
problems.
Definition-(b) Central Limit Theorem A statistical proposition to the
effect that the larger a sample size the more closely the *sampling
distribution of a sum or a mean will approach a *normal distribution.
This is true even if the population from which the sample is drawn is
not normally distributed. A sample size of 30 or more will usually
result in a sampling distribution for the mean that is very close to a
normal distribution. The central limit theorem explains why *sampling
error is smaller with a large sample than with a small sample and why we
can use the *normal distribution to study a wide variety of statistical
problems.
Please send your response to [log in to unmask]
As always, thank you for your responses!
Burke Johnson
http://www.southalabama.edu/coe/bset/johnson/dr_johnson/2vita.htm
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