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.
that you have a large variance in the population and/or your sample size is too small
In general the mean of a truly random sample is not dependent on the size of a sample. By inference, then, so is the variance and the standard deviation.
no
Yes it is.
df within
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.
that you have a large variance in the population and/or your sample size is too small
The sample variance is 1.
In general the mean of a truly random sample is not dependent on the size of a sample. By inference, then, so is the variance and the standard deviation.
n = sample sizen1 = sample 1 sizen2 = sample 2 size= sample meanμ0 = hypothesized population meanμ1 = population 1 meanμ2 = population 2 meanσ = population standard deviationσ2 = population variance
no
You have not defined M, but I will consider it is a statistic of the sample. For an random sample, the expected value of a statistic, will be a closer approximation to the parameter value of the population as the sample size increases. In more mathematical language, the measures of dispersion (standard deviation or variance) from the calculated statistic are expected to decrease as the sample size increases.
df within
Yes it is.
A random sample of size 36 is taken from a normal population with a known variance If the mean of the sample is 42.6. Find the left confidence limit for the population mean.
A small sample size and a large sample variance.
The larger your sample size, the less variance there will be. For instance, your information is going to be much more substantial if you took 1000 samples over 10 samples.