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Q: If the standard error of a sample of 48 is equal to 14.25 the standard deviation is equal to?

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The standard error is the standard deviation divided by the square root of the sample size.

If n = 1.

A sample of size 100.

From what ive gathered standard error is how relative to the population some data is, such as how relative an answer is to men or to women. The lower the standard error the more meaningful to the population the data is. Standard deviation is how different sets of data vary between each other, sort of like the mean. * * * * * Not true! Standard deviation is a property of the whole population or distribution. Standard error applies to a sample taken from the population and is an estimate for the standard deviation.

It simply means that you have a sample with a smaller variation than the population itself. In the case of random sample, it is possible.

Related questions

Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.

The standard error is the standard deviation divided by the square root of the sample size.

If the population standard deviation is sigma, then the estimate for the sample standard error for a sample of size n, is s = sigma*sqrt[n/(n-1)]

Let sigma = standard deviation. Standard error (of the sample mean) = sigma / square root of (n), where n is the sample size. Since you are dividing the standard deviation by a positive number greater than 1, the standard error is always smaller than the standard deviation.

True.

If n = 1.

2

Standard error of the mean (SEM) and standard deviation of the mean is the same thing. However, standard deviation is not the same as the SEM. To obtain SEM from the standard deviation, divide the standard deviation by the square root of the sample size.

There is no such thing. The standard error can be calculated for a sample of any size greater than 1.

the sample mean is used to derive the significance level.

The sample standard error.

The sample standard deviation (s) divided by the square root of the number of observations in the sample (n).

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