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Research Reproducibility

Reproducibility vs Replication

Reproducibility: “the ability of a researcher to duplicate the results of a prior study using the same materials as were used by the original investigator. That is, a second researcher might use the same raw data to build the same analysis files and implement the same statistical analysis in an attempt to yield the same results…. Reproducibility is a minimum necessary condition for a finding to be believable and informative.”

Replicability: "The ability of a researcher to duplicate the results of a prior study if the same procedures are followed but new data are collected."

Report of the Subcommittee on Replicability in Science Advisory Committee, - U.S. National Science Foundation, 2015


Reproducible research (computational reproducibility): Authors provide all the necessary data and the computer codes to run the analysis again, re-creating the results.

Replication: A study that arrives at the same scientific findings as another study, collecting new data (possibly with different methods), and completing new analyses.

Terminologies for reproducible research. 2018

Suggested Readings

Factors Influencing Reproducibility

Bishop's “Four Horsemen of Irreproducibility Apocalypse” - Main factors that may lead to irreproducible or false positive research: 

  1. Publication bias: Researchers are more likely to write about significant results and journal editors are more likely to accept manuscripts showing statistics significance or an expected result
  2. Low statistical power: Studies with low statistical power increase the likelihood that a statistically significant finding represents a false positive result.
  3. P-hacking: Running multiple studies but only reporting those that returned significant results.  Also known as data dredging or fishing expeditions.  
  4. HARKing (hypothesizing after results are known): When researchers state or change their hypothesis after results are analyzed. Also known as a post hoc hypothesis

               

(2018). Checklists work to improve science. Naturedoi: https://doi.org/10.1038/d41586-018-04590-7 ; Bishop, D. (2019). Rein in the four horsemen of irreproducibilityNature568(7753), 435-436.; Dumas-Mallet, E., Button, K. S., Boraud, T., Gonon, F., & Munafò, M. R. (2017). Low statistical power in biomedical science: a review of three human research domains. Royal Society Open Science4(2), 160254. ; Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review2(3), 196-217.

Reproduced from: https://libguides.tulane.edu/reproducibility under a Creative Commons Attribution-NonCommercial 4.0 International License.