A concerning amount of biomedical research is not reproducible. Unreliable results impede empirical progress in medical science, ultimately putting patients at risk. Many proximal causes of this irreproducibility have been identified, a major one being inappropriate statistical methods and analytical choices by investigators. Within this, we formally quantify the impact inappropriate redaction beyond a threshold value in biomedical science. This is effectively truncation of a data-set by removing extreme data points, and we elucidate its potential to accidentally or deliberately engineer a spurious result in significance testing. We demonstrate that the removal of a surprisingly small number of data points can be used to dramatically alter a result. It is unknown how often redaction bias occurs in the broader literature, but given the risk of distortion to the literature involved, we suggest that it must be studiously avoided, and mitigated with approaches to counteract any potential malign effects to the research quality of medical science.

The code herein consists of the Mathematica notebook file that faciliates direct derivation of the equations employed in the manuscript, and the raw data used to generate the illustrative examples (MATLAB / OCTAVE code).

Notebook can be executed as in inside Mathematica, or using Mathematica's stand alone player. The MATLAB .m files can be deployed in MATLAB or in OCTAVE.