posted by Sue .
I need so help figuring out which statement is true or false.
1. A parameter always describes a larger group than a statistic.
2. The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of all possible sizes from the same population.
3. Statistical inference allows one to find the exact value of a parameter.
1. True. Parameters are data from the total population, while statistics are data from samples.
2. True. A sampling distribution is the probability distribution, under repeated sampling of the population, of a given statistic (a numerical quantity calculated from the data values in a sample). The formula for the sampling distribution depends on the distribution of the population, the statistic being considered, and the sample size used. A more precise formulation would speak of the distribution of the statistic as that for all possible samples of a given size, not just "under repeated sampling". (From <(Broken Link Removed)
3. False. It is an estimate.
In the future, you can find the information you desire more quickly, if you use appropriate key words to do your own search. Also see http://hanlib.sou.edu/searchtools/.
I hope this helps. Thanks for asking.
I'll give you a few hints:
One is true; two are false.
The two false statements are false because of one word in each statement.
Identify the population and the sample:
A survey of 1200 credit card found that the average late fee is $24.3?