Answer :
The standard error for sample means is equal to the sample standard deviation when the sample size is more than 30 is a "false" statement.
Define the term standard error?
- When comparing a population mean to a sample mean, the standard error of mean, called simply standard error, shows how dissimilar the two are likely to be.
- It reveals how much doing the sample mean could change if a study were to be repeated with fresh samples drawn from a single population.
For the stated question-
Let's assume that our sample size is 30 and that the population standard deviation is 10.
standard error = 10/√30 = 1.83
Now let me use a sample size of 100 while keeping overall population standard deviation constant.
=10/10 = 1
It is obvious that with this sample size, the standard error will alter.
Therefore, when our sample size grows, the standard error will drop rather than stay the same.
Thus, the standard error for sample means is equal to the sample standard deviation when the sample size is more than 30 is a "false" statement.
To know more about the standard error, here
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