Only if you make it unbiased. Samples can be weird. If you make it unbiased, then yes.
A sample is Unbiased if everyone in the sample have an equal chance of being selected
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.
It is a biased estimator. S.R.S leads to a biased sample variance but i.i.d random sampling leads to a unbiased sample variance.
Biased- (Not random) Unbiased-(Random) Example: (ubbiased) Woman takes random people to take a survey.
The relations depend on what measures. The sample mean is an unbiased estimate for the population mean, with maximum likelihood. The sample maximum is a lower bound for the population maximum.
A sample is Unbiased if everyone in the sample have an equal chance of being selected
The proof that the sample variance is an unbiased estimator involves showing that, on average, the sample variance accurately estimates the true variance of the population from which the sample was drawn. This is achieved by demonstrating that the expected value of the sample variance equals the population variance, making it an unbiased estimator.
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.
It is a biased estimator. S.R.S leads to a biased sample variance but i.i.d random sampling leads to a unbiased sample variance.
No, it is biased.
They are samples from a population, but otherwise they are not similar.
Biased- (Not random) Unbiased-(Random) Example: (ubbiased) Woman takes random people to take a survey.
Enough data to be reprsentative Fair questions and appropriate answer choices or measure of answer An unbiased sample Conclusions that reflect the study accurately and not beyond the limits of the study.
Yes, there is a mathematical proof that demonstrates the unbiasedness of the sample variance. This proof shows that the expected value of the sample variance is equal to the population variance, making it an unbiased estimator.
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.
The relations depend on what measures. The sample mean is an unbiased estimate for the population mean, with maximum likelihood. The sample maximum is a lower bound for the population maximum.
The best point estimator of the population mean would be the sample mean.