You might be asking what about means?

When we talked bout doing inference for

means, we did not provide different formulas

for the standard error, when we were

calculating a confidence interval verses a hypothesis test.

But we seem to be making a pretty big

deal about it now that we're talking about proportions.

Well with means our parameter of interest is a Mu, and in our null hypothesis

we said Mu equal to some null value, but in our calculation of the standard error.

This is simply calculated as S over square root of n.

So Mu, our population mean, does not

appear in the calculation of standard error.

So it

really doesn't matter what that number is set equal to,

it's not going to influence the calculation of the standard error.

On the other hand, when you're, you're doing

a hypothesis test for proportion, we set p equal

to some null value, and that same p

does actually appear in calculation of the standard error.

And hence because it does appear in the calculation,

and because we must assume that the null hypothesis is

true when going through our calculations, we need to make a different distinction

between when we do have a null hypothesis that we must assume is true.

Within the context of hypothesis testing, versus

when we don't have a null hypothesis that

we must assume to be true, that is within the context of a confidence interval.

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Let's take a look real quick then.

Our conditions for inference met for conducting a

hypothesis test to compare the two proportions here?

In terms of the condition of independence, within group independence

we have a random sample and the 10% condition is met.

90 and 122 are obviously less than 10% of all males and females.

So, sampled males, in art can be assumed to be independent

of each other, as well as sampled females can

be assumed to be independent of each other, as well