That makes sense to me. I think I'm beginning to understand, thank you very much for your answers.
A quick question though: why are the degrees of freedom associated with the variance estimate? Is it linked to the definition of a "degree of freedom"?
Maybe you can use this example to explain me...
So if I understand well, this statement is wrong?
"Why do we have n-1 degrees of freedom? The reason is because to calculate the test statistic, we have used the sample mean as an estimator for the population mean µ"
Yes I understand that the variance has n-1 degrees of freedom because of the estimation of the sample mean.
BUT the t-distribution uses the estimate of the sample variance, which should remove ANOTHER degree of freedom?
I hope my misunderstanding is clear enough?
When we want to compute a confidence interval for the mean of a sample of n persons, it is always explained that the number of degrees of freedom used should be equal to the sample size minus the number of estimated parameters.
But, in my opinion, two parameters are estimated: the mean is...