1. The level of significance in hypothesis testing is the probability
of
	a. not rejecting a true null hypothesis
	b. no rejecting a false null hypothesis
	c. rejecting a true null hypothesis
	d. could be any of the above, depending on the situation
	e. none of the above answers is correct

2. In determining the same size [n] necessary to estimate a population
proportion[p], which of the following information is NOT needed?
	a. the maximum size of the sampling error that can be tolerated
	b. the confidence level required
	c. a preliminary estimate of the true population proportion[p]
	d. the mean of the population[u]
	e. none of the above answers is correct

3. if we consider the simple random sampling process as an experiment,
the sample mean[x] is
	a. always zero
	b. always smaller than the population mean[u]
	c. a random variable
	d. exactly equal to the population mean[u]
	e. none of the above answers is correct

4. A 95% confidence interval for a population mean[u] show the values
25.1 to 28.3.  What is the appropriate conclusion?
	a. 95% of all sample means[x] should be between 25.1 and 28.3
	b. 95% of the time the population mean[u] will be within this
interval: 5% of the time it will be outside of this interval
	c. Since 95% of all confidence intervals contain the population mean
[u], we are 95%  confident this interval include it
	d. All of the above answers is correct
	e. None of the above answers is correct

Hello icahn!
Here are the answers to your questions.

1. C
The level of significance is the probability of committing a Type I
error; while a Type I error is defined as rejecting the null
hypothesis when it's actually true. Please check the following page
for more information.

Hypothesis Testing
http://smccd.net/accounts/case/biol690/hypothesis.html

2. D
In the link that follows you can find the formula needed to calculate
the required sample sample size. Calculating this number implies that
you want to set a maximum sampling error (a) (as more people will
reduce this error); set a desired confidence level (b); and you also
need to have an estimate for the variance of the population. Since
we're talking about proportions, the variance is calculated as
n*p*(1-p), therefore, an estimate for p needs to be known. We don't
need the mean of the population, the sample proportion will naturally
tend to be "near" this number. Determining a sample size involves
setting a desires dispersion around the mean, but the mean is not
needed to calculate this dispersion.

How To Determine Sample Size
http://www.isixsigma.com/library/content/c000709.asp

3. C
In the following link (page 3) you can find why the sample mean is a
random variable. It's the sum of independent and identically
distributed random variables (each observation), divided by the sample
size, so it's itself a random variable.

http://giant.lakeheadu.ca/~wzhuang/4030/4030-lec8.pdf

4. E
None of the statements is correct. The correct answer would be that we
are 95% confident that the population mean is within this interval
(that's the definition of a confidence interval); however, this is not
due to the fact that "95% of all confidence intervals contain the
population mean", so answer C is not correct.


Google search strategy
"determine sample size"
"sample mean" "random variable"
"level of significance"


I hope this answers your question. If you have any doubt regarding my
answer, please request a clarification. Otherwise I await your rating
and final comments.


Best regards!
elmarto