More than half of US preclinical animal-model studies are considered irreproducible, with an estimated annual cost of $28–40 billion.1,2
While protocols may be clearly written, reproducibility often breaks down in execution. Small variations in technique, timing, and setup can lead to inconsistent results across teams and sites.
Where Things Go Wrong
Preclinical reproducibility issues rarely start with one dramatic failure. Written protocols leave out assumed knowledge. Shadowing varies by trainer and site. Method transfer between labs amplifies small differences until outcomes drift, especially when teams rely on written SOPs or informal shadowing.
This is why reproducibility in research commonly breaks down during day-to-day experimental work.
Where to Start: A Practical Framework
When methods become directly observable instead of loosely interpretable, reproducibility improves.
- ▪️ Pinpoint where variability enters the workflow. Look across operators, assay runs, instruments, and sites.
- ▪️ Capture tacit steps with visual protocols. Show timing, handling, setup, and technique, not just written instructions.
- ▪️ Standardize training inputs. Everyone should train on the same visual method instead of relying on informal shadowing. This supports standardizing experimental protocols and improving lab training efficiency.
- ▪️ Create one shared reference for standardizing workflows across teams. A video protocol library supports a cleaner method transfer between labs and fewer local “workarounds,” and faster ramp-up at new sites.
- ▪️ Track repeat experiments, troubleshooting time, and reagent waste. This helps quantify where small differences in execution are creating higher operational costs and delays.
Key Takeaway
Two major mistakes perpetuate the reproducibility crisis: underestimating training variability and treating reproducibility as a problem to optimize “later,” once issues become visible.
When teams stop relying only on written methods and start standardizing how experiments are seen and performed, reproducibility becomes much easier to control and major challenges down the line are significantly reduced.
Explore how leading labs are standardizing experimental methods with visual training.
- Freedman, L. P., Cockburn, I. M., & Simcoe, T. S. (2015). The economics of reproducibility in preclinical research. PLOS Biology, 13(6), e1002165. https://doi.org/10.1371/journal.pbio.1002165
- Bio-Rad Laboratories. (n.d.). Are costly experimental failures causing a reproducibility crisis? Insights and trends. Bio-Rad. https://www.bio-rad.com/en-za/applications-technologies/are-costly-experimental-failures-causing-reproducibility-crisis?ID=4ab22faf-bef3-cf71-fb92-2d603980d393