New method identifies the root causes of statistical outliers

Re:New method identifies the root causes of statistical outl... by magicalhippo on Tuesday July 19, 2022 @06:07PM (#32158611)

Lets say you time some code, and get a nice Gaussian at around 1ms say, but with a smaller peak far to the right (ie +20ms or so). The second peak is because your thread got scheduled. You don't care about those samples, they don't matter.

Can't you just say those are outliers and not consider them? Ideally you'd build some bimodal model, but given that they don't matter, what's wrong with throwing away those samples?

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Re:New method identifies the root causes of statistical outl... by kqr on Wednesday July 20, 2022 @03:46AM (#32162732)

This is the problem with how most people deal with outliers. You should absolutely care about your code being scheduled away. If you care about speed and then you change your code in ways that makes it get pre-empted more often, that matters!

Outliers are still useful data!

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Re:New method identifies the root causes of statistical outl... by magicalhippo on Wednesday July 20, 2022 @11:27AM (#32167156)

If you write code that gets pre-empted more often, then you've deliberately done that by inserting system calls or similar, and that would be an entirely different scenario. I agree that in such a scenario, it would not be an outlier.

[ Parent | on HN ]