1,7,1
no
That all observations are exactly the same.
The magnitude of difference between the statistic (point estimate) and the parameter (true state of nature), . This is estimated using the critical statistic and the standard error.
To calculate the standard error for a proportion, you can use the formula: [ SE = \sqrt{\frac{p(1 - p)}{n}} ] where (p) is the sample proportion and (n) is the sample size. If the proportion is not given in your question, you'll need to specify a value for (p) to compute the standard error. For a sample size of 25, substitute that value into the formula along with the specific proportion to find the standard error.
Measurement error: obviously!
A small sample size and a large sample variance.
true
no
That all observations are exactly the same.
The magnitude of difference between the statistic (point estimate) and the parameter (true state of nature), . This is estimated using the critical statistic and the standard error.
Standard error is random error, represented by a standard deviation. Sampling error is systematic error, represented by a bias in the mean.
It would help to know the standard error of the difference between what elements.
Standard error is a measure of precision.
The standard error is the standard deviation divided by the square root of the sample size.
Measurement error: obviously!
The standard error increases.
the purpose and function of standard error of mean