Research practice and reproducibility

Keep the research argument connected to its evidence

A statistical result is useful only if its inputs, analysis decisions, and interpretation can be understood and checked. Reproducibility links the scientific question to the data history, implementation, computational environment, and reported evidence.

Successful execution is one part of that record. It does not establish that a metadata join is correct, an exclusion is ignorable, or an inferential assumption holds. Good research practice makes those decisions visible so that collaborators and readers can assess them.

Two habits organize this guide. First, make the analysis a chain that another researcher can rebuild and audit, including the decisions that change its data and population. Second, make every written claim traceable to a result with a defined target, assumptions, and uncertainty. A reproducible mistake is still a mistake; a persuasive paragraph without recoverable evidence cannot be checked.

What you’ll learn

• Build an inspectable research record: connect data versions, identifiers, transformations, exclusions, code, configuration, environment, and retained outputs; then test the documented execution route in a fresh session.
• Report what the evidence supports: state the target and assumptions, distinguish estimates from performance summaries, label uncertainty, and link each claim to the analysis that produced it.

The checkpoints are completion targets. Additional practice is optional. The group’s published manuscripts supply research case studies; these pages use small synthetic examples to make the workflow and reporting arguments explicit.