Individual skill
ijoc-data-analysis
Use when running and reporting the computational experiments — and assembling the reproducible code/data deposit — for an INFORMS Journal on Computing (IJOC) manuscript. Turns a designed protocol (see ijoc-methods) into defensible, reproducible results; it does not redesign the experiment.
INFORMS-Journal-on-Computing-Skills/skills/ijoc-data-analysis/SKILL.md
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# Computational Experiments & Reproducibility (ijoc-data-analysis)
## When to trigger
- The protocol is designed and you are now **running experiments** and interpreting results
- A referee questions whether a performance win is **real** versus tuning, seed luck, or benchmark selection
- You need to turn raw outputs into the **statistical comparison** IJOC expects (not just a table of best times)
- You are assembling the **IJOC GitHub Software and Data Repository** deposit and want it to reproduce the paper
## Making the computational claim defensible
IJOC's distinctive risk is a result that is real on the page but an artifact underneath. Pre-empt the three ways a referee will attack it.
- **Tuning artifact.** Show the win holds with tuning done symmetrically on a disjoint set. Report performance at default *and* tuned for both your method and the baselines, so the gain is not hidden in hyperparameters.