that you have a large variance in the population and/or your sample size is too small
You cannot prove it because it is not true.The expected value of the sample variance is the population variance but that is not the same as the two measures being the same.
The n-1 indicates that the calculation is being expanded from a sample of a population to the entire population. Bessel's correction(the use of n − 1 instead of n in the formula) is where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance, and some (but not all) of the bias in the estimation of the population standard deviation. That is, when estimating the population variance and standard deviation from a sample when the population mean is unknown, the sample variance is a biased estimator of the population variance, and systematically underestimates it.
1
There is a brief table in Mario Triola's Elementary Statistics text. In the 9th edition it is on pages 354 - 355 with an example.
the small greek letter sigma squared.
that you have a large variance in the population and/or your sample size is too small
No, it is biased.
You cannot prove it because it is not true.The expected value of the sample variance is the population variance but that is not the same as the two measures being the same.
It's a lower-case Greek sigma followed by a superscript 2, in other words, "sigma-squared".
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.
(Population) variance = 6.4664
In statistics, this is the symbol for the "Variance"
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.
The n-1 indicates that the calculation is being expanded from a sample of a population to the entire population. Bessel's correction(the use of n − 1 instead of n in the formula) is where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance, and some (but not all) of the bias in the estimation of the population standard deviation. That is, when estimating the population variance and standard deviation from a sample when the population mean is unknown, the sample variance is a biased estimator of the population variance, and systematically underestimates it.
yes, it can be smaller, equal or larger to the true value of the population varience.
1