 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.^[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.