sharkdp/hyperfine

Confidence intervals, p-values

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#523 opened on Jun 16, 2022

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 (3 comments) (3 reactions) (0 assignees)Rust (28,110 stars) (479 forks)batch import
feature-requesthelp wanted

Description

First, nice job on Hyperfine!

I use it in academic research, where I'll be computing p-values to describe the confidence with which the sample data suggests that one program is faster than another. (I.e. how confident are we that the mean speed of the fastest program is actually different from the mean speed of the other programs.)

Would you be open to a pull request that adds such a calculation to Hyperfine?

On a related note, I am not sure how to interpret 1.40 ± 0.03 times faster in Hyperfine output. From the source code, it looks like a calculation based on one standard deviation. Is this meant to say, e.g., that if the same invocation of Hyperfine were done 100 times, that we expect 68% of those outcomes to say that program A is between 1.37 and 1.43 times as fast as program B (where 68% corresponds to one sigma)? Is it a confidence interval?

Thanks in advance for any clarification you can provide!

Contributor guide

Tech stack
rust
Domain
cliperformance
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Rust basicsStatistics fundamentals
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
35
Research direction
Examine the current implementation of comparison statistics in the hyperfine codebase, specifically in `src/benchmark.rs` or `src/outlier.rs` where the '±' notation is computed. Understand the existing one standard deviation approach and identify where to add confidence interval and p value calculations. Consider using a t test or bootstrap method, and review the 3 existing comments for maintainer guidance.