Benchmarking Node.js looks simple, but getting meaningful results is still surprisingly hard. This talk explains why many Node.js benchmarks continue to produce misleading conclusions, even when they appear careful and well-intentioned.

We walk through common benchmarking pitfalls, including unrealistic workloads, unstable environments, overreliance on microbenchmarks, and misuse of statistics. The session shows how small methodological choices can dramatically change results, leading teams to optimize the wrong things or draw incorrect conclusions about performance.

The talk also presents better approaches. It covers how to design benchmarks that are closer to real applications, how to control noise, how to validate results using basic statistical techniques, and how to reason about performance changes responsibly. Throughout the session, real Node.js examples are used to show where benchmarks go wrong and how they can be improved.

The goal is to help developers and teams build a healthier mental model of Node.js performance, so benchmarks become a useful tool rather than a source of confusion.

This talk focuses on common mistakes in Node.js benchmarking and shares practical guidance on designing, running, and interpreting benchmarks more reliably