Clever Geek Handbook

Am I using z-score or t-score for my confidence interval?

I would like to estimate the average of the dataset that I have.

I have 1000 data points and I read somewhere that if your sample size is less than 30 you should use t-count, otherwise use z estimate.

Here is the code I am using

def mean_confidence_interval(data,confidence = 0.95):

    from numpy import mean,array
    import scipy as sp
    import scipy.stats

    a = array(data)

    n = len(a)
    m, se = mean(a), scipy.stats.sem(a)
    h = se*sp.stats.t._ppf( (1+confidence)/2., n-1)

    return m, h, (m-h,m+h)

I am wondering what function can I use insteaf sp.stats.t._ppf

to calculate the correct z value.

+3


source to share


1 answer


You use the z-score / test when the population standard deviation is known and the t-score / test when it is estimated from the data. For large samples (~ 30) they become the same. So in your case, I would just use your t-score confidence intervals for everything.



+1


source






More articles:


All Articles