One exciting study is not the whole answer

Understand the idea

Under the National Academies' definitions, computational reproducibility uses the same data and analysis, while replicability asks whether new studies with new data obtain consistent results. A failure can reveal mistakes, contextual differences, or a fragile finding; it does not automatically prove fraud. [S05] A p-value is conditional on a statistical model and is not the probability that a hypothesis is true. Evidence appraisal also needs study design, effect size, uncertainty, and the broader body of research. [S06]

Worked example

A fictional learning intervention improves a test by two points in a large study. A second study estimates one point with considerable uncertainty. Ask whether this is a direct contradiction, what a two-point effect means educationally, and whether a reproducible spreadsheet would settle the intervention's real-world value.

Where the idea stops

Fields use these terms differently. Neither one successful replication nor an arbitrary significance cutoff establishes practical importance or universality. [S05, S06]

Connect it

Psychology: what a robust learning claim should require.

Go deeper

Can an effect be statistically detectable but practically unimportant?

Sources