The sample mean is an unbiased estimator of the population mean because the average of all the possible sample means of size n is equal to the population mean.
True.
The population mean is the mean value of the entire population. Contrast this with sample mean, which is the mean value of a sample of the population.
With a good sample, the sample mean gets closer to the population mean.
That the key characteristics of the population are reflected in the sample.
The sample mean is an unbiased estimator of the population mean because the average of all the possible sample means of size n is equal to the population mean.
True.
Provided the samples are independent, the Central Limit Theorem will ensure that the sample means will be distributed approximately normally with mean equal to the population mean.
The sample is not a perfect representation of the population.
It means that the every element in a population has an equal chance of being selected to be in the sample which is studied. Equivalently, in considering a sample of a particular size, every possible sample of that size has the same chance of being selected.
The population mean is the mean value of the entire population. Contrast this with sample mean, which is the mean value of a sample of the population.
It means you can take a measure of the variance of the sample and expect that result to be consistent for the entire population, and the sample is a valid representation for/of the population and does not influence that measure of the population.
It means that every member of the population has the same probability of being included in the sample.
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.
The best estimator of the population mean is the sample mean. It is unbiased and efficient, making it a reliable estimator when looking to estimate the population mean from a sample.
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.
With a good sample, the sample mean gets closer to the population mean.