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Math Help - Z-test vs. t-test and calculating std error

  1. #1
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    Z-test vs. t-test and calculating std error

    Hello,


    I have two questions concerning proportions.


    First, say I have one sample of 1000 units and 10 percent have the characteristic A while 8 percent have B - how would I test whether these two proportions are significantly different, is A actually more common than B? I understand that I can compute a z-test, but how would I calculate the standard error for this test?


    What I can find (say, Agresti & Finlay) either seems to describe comparing a proportion in one sample to some population parameter my, or comparing proportions from two different samples but in my case, I'm dealing with one sample only.


    And more generally, is it correct that when comparing proportions I would use a z-test, not a t-test (if I'm wrong, how would I compute the standard error in a t-test?).


    For any help, thanks in advance,
    deenha
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  2. #2
    Grand Panjandrum
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    Quote Originally Posted by deenha View Post
    Hello,


    I have two questions concerning proportions.


    First, say I have one sample of 1000 units and 10 percent have the characteristic A while 8 percent have B - how would I test whether these two proportions are significantly different, is A actually more common than B? I understand that I can compute a z-test, but how would I calculate the standard error for this test?


    What I can find (say, Agresti & Finlay) either seems to describe comparing a proportion in one sample to some population parameter my, or comparing proportions from two different samples – but in my case, I'm dealing with one sample only.


    And more generally, is it correct that when comparing proportions I would use a z-test, not a t-test (if I'm wrong, how would I compute the standard error in a t-test?).


    For any help, thanks in advance,
    deenha
    Think \chi ^2 test, you have an observed distribution 100,\ 80,\ 820 for the three outcomes A, B and C (neither of A or B). Under your null hypothesis the expected distribution is 90,\ 90,\ 820.

    CB
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