How can we ignore missing values or NaN during testing? A = [0.1, NaN, 0.16] B = [1.0, 0.9, 0.82] EqualVarianceTTest(A, B) output: Two sample t-test (equal variance) ---------------------------------- Population details: parameter of interest: Mean difference value under h_0: 0…

Easy to ignore whatever values you don’t want to use: EqualVarianceTTest(filter(!isnan, A), B). If your observations in A and B are paired and you want to ignore corresponding elements from both (not only remove A[2] that is NaN but also remove B[2]), then it’s most convenient to put these arrays i…

`NaN` in Statistical Hypothesis Testing

New to Julia

Sukera September 29, 2023, 11:55am 2

(I don’t know the answer to your question, but I adjusted the title a bit to make it clear to others that this is a question about statistical testing, not unit testing!)

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`NaN` in Statistical Hypothesis Testing

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