ChatGPT also says D. It says something about sensitivity, but sensitivity wouldn't be an issue if ks test is applied correctly. This question is tricky, how can we argue js is more robust than ks test, surely this is subjective. An answer below says js produces a score below 0 and 1, but ks test can also do so. I think if I was asked this question in the exam I will just go with D because of Chat GPT but if I were to make a guess I'd say C.
The statement in option D is claiming the exact opposite of what is true - it's stating that JS is more robust with large datasets when in fact KS is the more robust choice for large datasets.
This is a key advantage of using Jensen-Shannon divergence. It produces a value between 0 and 1, which represents the divergence between two distributions. This value can be interpreted without needing to set arbitrary thresholds or cutoffs. In contrast, the KS test involves comparing the test statistic to a critical value, which can depend on the significance level chosen.
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