A WORKING DEFINITION OF SCIENTIFIC RIGOR
A PENTATEUCH FOR SCIENTIFIC RIGOR
(i) Redundancy in experimental design.
|Elementa||Description and implementation|
|Replication||Carry out independent replicates, which provide information regarding the replicability and variability of an observation. This in turn influences the interpretation of the magnitude of the observed effects and the sample size required for statistical significance.|
|Validation||Validate the observation by an independent methodology. For example, the assignment of a protein on an immunoblot can be validated by immunoprecipitation using different antibodies, and the differential abundance of an mRNA by microarray or transcriptome sequencing can be validated by reverse transcription-quantitative PCR. This element also applies to purifications, which should use at least two independent methodologies. For example, chromatography can be complemented with differential sedimentation, electrofocusing, precipitation, etc. The element of validation is particularly important for experimental components such as antibodies and cell lines.|
|Generalization||Explore the generalizability of the findings. For example, in microbiological studies the use of different strains, cell lines, reagents, media, experimental conditions, etc., can be used to ascertain the generalizability of the finding. Findings that are generalizable are more likely to be robust.|
|Perturbation||Define the conditions under which the observation occurs by perturbing the system. For example, before reporting a biochemical observation, perturb the system by changing the pH or the ionic strength of the experimental conditions. Knowledge of the perturbation boundaries introduces redundancy since it inevitably includes replication and generalization and reveals the degree of resiliency, which in turn enhances the likelihood of replication.|
|Consistency||Determine whether the various observations that define a scientific study are internally consistent. Although internal consistency does not necessarily imply validity, its absence may suggest the presence of uncontrolled variables.|
(ii) Sound statistical analysis.
(iii) Recognition of error.
(iv) Avoidance of logical traps.
(v) Intellectual honesty.
RIGOR AND REPRODUCIBILITY
ENHANCING RIGOR IN BIOMEDICAL RESEARCH TRAINING
(i) Develop didactic programs to teach the elements of good experimental practices.
(ii) Require formal training in statistics and probability for all biomedical scientists.
(iii) Refocus journal club discussion from findings to methods and approaches.
(iv) Develop continuing education materials for biomedical sciences.
(v) Develop teaching aids to enhance the quality of peer review.
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