R / Python置信区间

Question

Just trying to figure out a R -> Python thing: Why are these two items not giving the same results?

Calculating 95% confidence interval for sample data with mean = 65, s = 22, n = 121.

R: tsum.test(nx=121, mean.x=65, sx=22)

gives 95% confidence interval of 61.04014 68.95986

Python: stats.norm.interval(alpha=0.95, loc=65, scale=22/np.sqrt(121))

gives 95% confidence interval of (61.080072030919894, 68.9199279690801)

I thought that these should be identical results, or am I not using the appropriate equivalent Python function for R's tsum.test ?

Answer 1

Upon further investigation I can see that I was wrong to assume to use stats.norm for this.

scipy.stats.t allows for the degree of freedom calculation that R's tsum.test is doing automatically:

stats.t.interval(alpha = 0.95, df = 121-1, loc = 65, scale= 22/np.sqrt(121)) returns (61.04013918989445, 68.95986081010555)

which round at 5 decimal points to the answers given by tsum.test in R.

The general function I am using, if helpful, is this:

def get_conf_interval_from_sample(n, mean, sigma, alpha = 0.95) :
    """Get confidence interval from sample data with sample of n, mean, sigma, where df = n-1
    Equivalent to getting confidence interval using t.test / tsum.test in R"""
    df = n-1
    scale = sigma / np.sqrt(n)
    return stats.t.interval(alpha=alpha, df=df, loc=mean, scale=scale)````