Murphy
-
Best practices for version control of research datasets, not just code
Method Help
Researchers usually version‑control code but often forget datasets. Data evolves through cleaning and processing steps, and version mismatches can break reproducibility. What conventions help you track dataset versions properly?
Donald Thomas, Dennis Campbell and 23 others4 CommentsView more comments-
Keep raw data separate from any processed or cleaned versions from day one.
2 -
Record every transformation step so intermediate versions stay traceable.
2 - View more comments
-
-
How to avoid over‑interpreting statistically marginal experimental results
Method Help
It is tempting to draw strong conclusions from borderline‑significant experimental outcomes. Over‑interpretation of weak statistics remains a pervasive problem across many disciplines. What practical habits help you stay disciplined?
View more comments-
Pre‑register your analysis plan whenever possible before you look at experimental outputs.
2 -
Do not treat p‑values as sharp binary pass‑fail cutoffs.
2 - View more comments
-
