If the minimum value is the minimum observed value then it indicates that the distribution goes below the minimum observed value.
If the minimum value is the minimum defined for the distribution then it indicates that
Green triangle in top left corner.
its a percent error * * * * * No, it is the relative error. When that is multiplied by 100 it becomes a percentage error.
It should but it probably will not because of: experimental error measurement error calibration error (zero error)
Human error.
A genetic error is an genetic disease
The standard error is the standard deviation divided by the square root of the sample size.
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.
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.
standard error
No.
If n = 1.
There is a calculation error.
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.
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Standard error is random error, represented by a standard deviation. Sampling error is systematic error, represented by a bias in the mean.
You calculate the standard error using the data.
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.