The measurements for all members of the sample are the same.
Your single number is your only information of the median value of the population, so the median value is the same as your single number. It is also the mode and mean of your sample.
No, the standard deviation is a measure of the entire population. The sample standard deviation is an unbiased estimator of the population. It is different in notation and is written as 's' as opposed to the greek letter sigma. Mathematically the difference is a factor of n/(n-1) in the variance of the sample. As you can see the value is greater than 1 so it will increase the value you get for your sample mean. Essentially, this covers for the fact that you are unlikely to obtain the full population variation when you sample.
could a sample set have the same range but different means
In the context of a sample of size n out of a population of N, any sample of size n has the same probability of being selected. This is equivalent to the statement that any member of the population has the same probability of being included in the sample.
Assuming that other measures remain the same, as the sample estimate increases both ends of the confidence interval will increase. In effect, the confidence interval will be translated to a higher value without any change in its size.Assuming that other measures remain the same, as the sample estimate increases both ends of the confidence interval will increase. In effect, the confidence interval will be translated to a higher value without any change in its size.Assuming that other measures remain the same, as the sample estimate increases both ends of the confidence interval will increase. In effect, the confidence interval will be translated to a higher value without any change in its size.Assuming that other measures remain the same, as the sample estimate increases both ends of the confidence interval will increase. In effect, the confidence interval will be translated to a higher value without any change in its size.
The sample mean is distributed with the same mean as the popualtion mean. If the popolation variance is s2 then the sample mean has a variance is s2/n. As n increases, the distribution of the sample mean gets closer to a Gaussian - ie Normal - distribution. This is the basis of the Central Limit Theorem which is important for hypothesis testing.
You will typically have an experimental parameter that will be varied as part of testing a hypothesis.
A biopsy is a sample of tissue that is sent to the lab for testing. A blood test is testing of the blood itself.
The value of x is directly proportional to to the value of y.hence when the value of x increases the value of y decrteses and vice verse
The rate of nuclear decay increases as the temperature of a radioactive sample increases. This is due to the increased kinetic energy of the nuclei at higher temperatures, which facilitates interactions that lead to nuclear decay.
A 'control' is a sample with a known outcome. By testing the control at the same time, with the same operator, under the same conditions as the 'test sample' one builds validity into the test result, assuming of course, that the result gives the expected outcome.
The density and chemical composition of both the large sample and a smaller piece of solid calcium sulfate are the same.
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.
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.
Try it out. 20 - 3 = 17 20 - 4 = 16 The value decreases as x increases.
The F distribution is used to test whether two population variances are the same. The sampled populations must follow the normal distribution. Therefore, as the sample size increases, the F distribution approaches the normal distribution.