= Scientific reproducibility
<Scientific reproducibility> concerns whether the basis of a scientific result can be reconstructed and checked. It requires enough information about the <measurement>, data handling and reasoning to distinguish repeating a procedure from merely obtaining a similar-looking answer.
Terminology varies between fields. The 2019 National Academies report distinguishes <computational reproducibility>, using the same data and computational procedure, from <scientific replication>, which collects new data to address the same scientific question. These test different links in the argument. Repeating a calculation can expose a software or transcription error; new observations can test whether the finding persists beyond the original sample.<Reproducibility and Replicability in Science by National Academies of Sciences, Engineering, and Medicine>[Summary, pp. 6–9: Definitions, Reproducibility and the distinction from new-data replication]{ref}
Consider an original toy measurement of three lengths, $10$, $11$ and $12$ units. Recomputing their <arithmetic mean> as $11$ checks the recorded calculation. Measuring three new objects tests a different question: whether the estimated <mean> describes further observations, allowing for <measurement uncertainty> and <sampling variation>. Neither exercise alone establishes that the instrument was correctly calibrated.
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