Reading the evidence
Replication repeats a study or test using the same method, independent implementation, new data or a new setting. Each form checks a different source of error.
Make it click
A recipe gains credibility when another kitchen follows it and obtains a comparable result. Using the same cook and ingredients checks less than an independent attempt.
Now put that into markets
Code reproduction can catch implementation errors; new periods and datasets test generalisation. Results need not match exactly to be informative.
A replication should specify acceptable differences and preserve failed attempts. Meta-analysis combines compatible estimates while respecting heterogeneity.
Compare protocol, population, measurement and independence. Report effect and uncertainty rather than a binary success label.
One failure can reflect low power or changed conditions, while repeated success can share the same hidden bias. Independence matters.
Takeaway
