how to test a hypothesis with t-test or p value

Hello I am testing the null hypothesis mu=3.4 against mu>3.4, for a sample of size 9. I want to test this with both t-distribution table and p-value but its giving me NaN instead of 0 or 1.this is the code im using :

mu = 3.4;     % Population mean

sample=[3.4,3.6,3.8, 3.3,3.4,3.5,3.7,3.6,3.7]

n = numel(sample)

xobs = mean(sample)  % Sample mean

s = std(sample)      % Sample standard deviation

t = (xobs - mu)/(s/sqrt(n))

p = 1-tcdf(t,n-1)

[h,ptest] = ttest(xobs,mu,0.05,'right')

Thank you

Answer 1

[the cyclist]

I can't look at this fully right now, but one thing I notice immediately is that you are putting the mean value of the sample as the first argument of ttest(), but you should be putting the sample itself:

ttest(sample,mu,0.05,'right')

I don't know if that is the only problem with your code, but is is definitely a problem with it. :-)

[farfar]

U are right! at least it is giving me the result that I need now, thank you !

[farfar]

I have a question now. what if i am not given the sample and I just have the mean value of sample. If i put mean in [h,ptest] = ttest(mean,mu,0.05,'right'), it is giving me "not a number" error again.

[the cyclist]

You can't do a t-test with just the sample mean. You need the distribution. This is pretty obvious if you consider the two samples

sample = [10 10 10 10 10 10 10];

and

sample = [-8 0 7 10 13 20 28];

which have the same mean, but obviously different likelihood of being different from the population mean.

The specific reason you get NaN from what you are doing is that you are putting in a "sample" of one value, and the t-test is ill-defined for a sample size of one.

[farfar]

Tnx!