Slides of my talk at the FENS Satellite-Event ‘How reproducible are your data?’ at Freie Universität, 6. July 2018, Berlin

1. Let’s get this out of the way: Reproducibility is a cornerstone of science: Bacon, Boyle, Popper, Rheinberger
2. A ‘lexicon’ of reproducibility: Goodman et al.
3. What do we mean by ‘reproducible’? Open Science collaboration, Psychology replication
4. Reproducible – non reproducible – A false dichotomy: Sizeless science, almost as bad as ‘significant vs non-significant’
5. The emptiness of failed replication? How informative is non-replication?
6. Hidden moderators – Contextual sensitivity – Tacit knowledge
7. “Standardization fallacy”: Low external validity, poor reproducibility
8. The stigma of nonreplication (‘incompetence’)- The stigma of the replicator (‘boring science’).
9. How likely is strict replication?
10. Non-reproducibility must occur at the scientific frontier: Low base rate (prior probability), low hanging fruit already picked: Many false positives – non-reproducibility
11. Confirmation – weeding out the false positives of exploration
12. Reward the replicators and the replicated – fund replications. Do not stigmatize non-replication, or the replicators.
13. Resolving the tension: The Siamese Twins of discovery & replication
14. Conclusion: No scientific progress without nonreproducibility: Essential non-reproducibility vs . detrimental non-reproducibility
15. Further reading

Open Science Collaboration, Psychology Replication, Science. 2015 ;349(6251):aac4716

Goodman et al. Sci Transl Med. 2016;8:341ps12.

https://dirnagl.com/2018/05/16/can-non-replication-be-a-sin/

https://dirnagl.com/2017/04/13/how-original-are-your-scientific-hypotheses-really