Computational reproducibility means obtaining consistent results from the same input data, computational procedure and stated conditions. A reproducible analysis identifies the actual inputs and their data provenance, the software versions, parameters and relevant environment. Exact bitwise agreement is stronger than agreement within a justified numerical tolerance.[ref]
For example, changing the order of a floating point sum can change its last few digits through rounding error. A useful check states the acceptable absolute error or relative error before comparing results. Successfully rerunning the same code also reruns any undetected mistake in it, so consistency is evidence about the computational process rather than a proof that the physical model is correct.
The recorded origins and transformations of data, including references, versions, preprocessing and identifiers.
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