Before a Computational Experiment
A short checklist for turning an interesting question into an experiment whose result can still be understood weeks later.
A computational experiment often begins with code, but the most important decisions usually happen before the first command runs. A few minutes spent defining the question can save hours of producing results that look precise but answer nothing in particular.
Write down the claim
I try to express the experiment as one sentence: if a specific condition changes, a measurable outcome should change in a particular way. This sentence does not need to be elegant. It needs to be falsifiable.
The claim separates the object of study from the machinery used to study it. A model, library, or cluster is part of the method. It is not the research question.
A result becomes easier to trust when the expected observation was written before the result appeared.
Fix the comparison
Every experiment needs a reference point. It may be a simple baseline, a previous method, an analytical result, or an intentionally naive implementation. Without that reference, improvement has no stable meaning.
Before running anything, I record:
- which inputs remain fixed;
- which variable is allowed to change;
- which metric will represent the outcome;
- what result would count as a meaningful difference.
This also exposes hidden degrees of freedom. If too many choices remain open, it becomes easy to adjust the experiment after seeing the results.
Decide what to preserve
An experiment is not only its final figure. The input snapshot, configuration, code revision, environment information, and raw output all help explain how that figure came into existence.
I prefer a small run directory with a readable name and a timestamp. It contains the exact configuration, a log, and the generated result. The directory should be understandable without relying on terminal history or memory.
Run the smallest useful version
The first run should test the pipeline, not prove the whole idea. A small dataset or a narrow parameter range is usually enough to reveal missing fields, unstable assumptions, or outputs that cannot be interpreted.
Only after that pilot produces a coherent record do I scale the experiment. The goal is not caution for its own sake. The goal is to make every expensive run carry information.
Closing note
Good experiments feel slower at the beginning because they force decisions into the open. They become faster later because the output has a known question, a known comparison, and a traceable origin.