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First of all, thanks for taking the time to write the awesome introduction! I've learned a lot :)
I was reading the code and the error Error::ZeroVariancereturned here got me thinking: does it make sense for confidence_interval() to only return this error when both the samples have a zero variance?
Numerically, when both are zero we have a problem (v would divide by zero). But when only one is zero (let's say x.var), the code would collapse to:
// Estimate the variance of the `y.mean - x.mean`let var_delta = y.mean_var();// Approximate the degrees of freedom of `var_delta`let v = (y.count - 1)asf64;
The text was updated successfully, but these errors were encountered:
Thanks for taking a look at the code! I think what you’re saying makes sense. I can imagine a situation where one of the benchmarks is so reliable that it always gives the same answer (ie. the true distribution is a delta function). In this case I think the CI for the difference in the means should just be the CI for the mean of the unreliable benchmark with an offset.
Hello,
First of all, thanks for taking the time to write the awesome introduction! I've learned a lot :)
I was reading the code and the error
Error::ZeroVariance
returned here got me thinking: does it make sense forconfidence_interval()
to only return this error when both the samples have a zero variance?Numerically, when both are zero we have a problem (
v
would divide by zero). But when only one is zero (let's sayx.var
), the code would collapse to:The text was updated successfully, but these errors were encountered: