This community-built FAQ covers the “Standard Error” exercise from the lesson “Sampling Distributions”.
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This exercise can be found in the following Codecademy content:
Master Statistics with Python
FAQs on the exercise Standard Error
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I feel like I am missing something. This reads: “The standard deviation of a sampling distribution is also known as the
standard error of the estimate of the mean. In many instances, we cannot know the population standard deviation, so we estimate the standard error using the sample standard deviation: standard deviation of our sample decided by the root of the sample size.” Yet, the second part of the Central Limit Theorem is that the" sampling distribution of the mean is normally distributed, with standard deviation equal to the population standard deviation, sigma, divided by square root of sample size (n)"
Does this imply that, if I don’t know the population standard deviation and I calculate the Standard Error of the estimate of the mean, I could replace the Population Standard Deviation with that result(Standard Error) as well?
No, you’re not going to know the standard deviation of the population, it’s an estimate. You have sample data to estimate it (and other characteristics) and extrapolate that to the population as a whole.