The population mean is the mean calculated over every member of the set of subjects being studied. It is usually not available and a survey is used to find an estimate for the population mean. The mean value of the variable in question, calculated from only the subjects included in the sample (or survey) is the sample mean. Provided some basic statistical requirements are met, the sample mean is a "good" estimate of the population mean.
Zero
Sampling bias.
Random error.
A parameter is a number describing something about a whole population. eg population mean or mode. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for a population parameter. (because samples should represent populations!)
The answer depends on the underlying variance (standard deviation) in the population, the size of the sample and the procedure used to select the sample.
Zero
You calculate the actual sample mean, and from that number, you then estimate the probable mean (or the range) of the population from which that sample was drawn.
Sampling bias.
Sampling Error
standard error
The same basic formula is used to calculate the sample or population mean. The sample mean is x bar and the population mean is mu. Add all the values in the sample or population and divide by the number of data values.
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
Random error.
A parameter is a number describing something about a whole population. eg population mean or mode. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for a population parameter. (because samples should represent populations!)
The answer depends on the underlying variance (standard deviation) in the population, the size of the sample and the procedure used to select the sample.
For a population the mean and the expected value are just two names for the same thing. For a sample the mean is the same as the average and no expected value exists.
Sampling distribution is the probability distribution of a given sample statistic. For example, the sample mean. We could take many samples of size k and look at the mean of each of those. The means would form a distribution and that distribution has a mean, a variance and standard deviation. Now the population only has one mean, so we can't do this. Population distribution can refer to how some quality of the population is distributed among the population.