I have a homework in which I am using data from NFL teams. This information was taken from the ESPN site and I was asked to calculate the following: $mean$, $standard$ $deviation$ and $standard$ $error$ of certain data. I know that there are 32 teams so I am taking data $(32$ $numbers)$ which I suppose represent the entire population and not a sample. This means that I have 32 "numbers" to take into account in order to make the calculations.
The mean is easy to get because I only add the 32 $numbers$ and divide the total by 32.
Standard deviation can be calculated for the population and for a sample as follows:
$\sigma = \sqrt{\frac{\sum_1^N(x_i - \mu)^2}{N}}$
$s = \sqrt{\frac{\sum_1^n(x_i - \bar{x})^2}{n - 1}}$
I am also asked to calculate $standard$ $error$ which I understand is the standard deviation of the sampling distribution.
I am not sure how to proceed.
Which formula should I use to calculate standard deviation, population or sample? I am actually using the population mean formula because I think I have all the data and not a sample.
How will I calculate standard error?
Technically, the standard error is the standard deviation of an estimator. Most commonly, this refers to sample mean $\bar X$ as an estimator of the population mean $\mu.$
So the 'standard error of the mean' is $SD(\bar X) = \sigma/\sqrt{n}.$ If $\sigma$ is unknown, it is estimated as the sample standard deviation $S.$ This means that the '(estimated) standard error' is $S/\sqrt{n}.$
Minitab's
describeprocedure for 32 observations, includingSE Mean, can be illustrated as follows:In this instance the 32 observations are treated as a sample, using your formula for $S$ to get 8.97. Then
SE Meanis $8.97/\sqrt{32} = 1.585687 \approx 1.59.$In your case, where you are using the entire sample, I would use $\sigma = 8.83$ from your formula for $\sigma$ and divide by $\sqrt{32}$ to get $1.65.$ But I would also explain that I used the formula for $\sigma$ instead of the formula for $s$ because I am working with the whole population instead of a sample.