# What is the purpose of sampling errors

## Statistics question: standard errors and sampling errors

Hello

I have a basic statistics question. Wikipedia does not distinguish between standard errors and sampling errors.

But that's exactly what we did in class. Unfortunately, we were only given the formulas without an explanation (BA .. there is obviously no time for something like this or is viewed as prior knowledge, no idea). Now in post-production I somehow don't get it.

Our formulas from the folder:

or the short form we work with:

where lambda stands for the normal distribution. N = large population, p, q are the proportions and n is the sample.

In class, for example, we calculated the standard error as follows: 100 items, 90 of which are OK and 10 are defective

-->

According to the lessons, the standard error is needed first to determine the sampling error. According to Wikipedia, both would be the same anyway (click they also have a completely different formula) and according to other internet sources, the calculation of the standard error requires the sample error. Total chaos.

Therefore, first of all a question whether I am right with my understanding of the terms.

indicates the dispersion of the sample means around an overall mean. Example: Determination of age!

I do a sample and take the mean. Then a second sample, this time the mean will differ slightly. Even with a third or more random sample, the mean will differ somewhat. If I now form a mean from these sample mean values (total mean value), then the standard error is the spread of the individual sample mean values from the total mean value. The standard error becomes smaller, the larger the sample size, since I can of course estimate a more exact total MW with more sample values.

In short: Can someone explain to me in the simplest possible terms the content and relationship between standard and sample errors?

A link to another forum may be sufficient.

Thanks and regards

Martin

I have a basic statistics question. Wikipedia does not distinguish between standard errors and sampling errors.

But that's exactly what we did in class. Unfortunately, we were only given the formulas without an explanation (BA .. there is obviously no time for something like this or is viewed as prior knowledge, no idea). Now in post-production I somehow don't get it.

Our formulas from the folder:

**1.**Standard error =or the short form we work with:

**2.**Sampling error = lambda * standard errorwhere lambda stands for the normal distribution. N = large population, p, q are the proportions and n is the sample.

In class, for example, we calculated the standard error as follows: 100 items, 90 of which are OK and 10 are defective

-->

According to the lessons, the standard error is needed first to determine the sampling error. According to Wikipedia, both would be the same anyway (click they also have a completely different formula) and according to other internet sources, the calculation of the standard error requires the sample error. Total chaos.

Therefore, first of all a question whether I am right with my understanding of the terms.

__Standard error:__indicates the dispersion of the sample means around an overall mean. Example: Determination of age!

I do a sample and take the mean. Then a second sample, this time the mean will differ slightly. Even with a third or more random sample, the mean will differ somewhat. If I now form a mean from these sample mean values (total mean value), then the standard error is the spread of the individual sample mean values from the total mean value. The standard error becomes smaller, the larger the sample size, since I can of course estimate a more exact total MW with more sample values.

__Sampling error:__I have no idea. According to internet sources, the above description is more likely to apply to the sampling error.In short: Can someone explain to me in the simplest possible terms the content and relationship between standard and sample errors?

A link to another forum may be sufficient.

Thanks and regards

Martin

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