HypotheSci Philosophy
Benefits (summary)
- Faster alignment on what to fix first in high-friction lab workflows.
- Clearer confidence in data used for scientific and operational decisions.
Follow-on benefits (plausible)
- More reproducible handoffs across teams.
- Lower rework from fragile workflow assumptions.
- Better readiness for internal and external review.
Concepts: Data Trust, Integration, Uncertainty
Related: Our Approach, 90-Minute Lab Assessment
Problem
Most lab teams do not fail because they lack tools. They fail because workflows become opaque: data moves through too many handoffs, assumptions are undocumented, and nobody can quickly explain why a result should be trusted.
Solution
HypotheSci applies a scientific method to informatics decisions. We map workflow behavior, define testable hypotheses about failure modes, run constrained pilots, and expand only after evidence shows measurable improvement.
Benefits
- You can prioritize changes using observed risk instead of opinion.
- You can communicate tradeoffs clearly to scientists, managers, and technical stakeholders.
- You can move faster while preserving evidence quality.
Method
- Observe the workflow as it is, including handoffs and failure points.
- Hypothesize which changes will reduce risk or friction.
- Test with narrow scope and explicit success criteria.
- Adjust based on evidence and preserve what works in repeatable playbooks.
Evidence
We favor claims that can be checked: baseline symptoms, pilot outcomes, documented constraints, and explicit follow-on assumptions.
Constraints
No universal blueprint fits every lab. Outcomes depend on data quality, operational maturity, and how consistently teams execute the agreed method.
CTA
Request a workflow reliability sprint
Start with the sample-chain checklist
Implementation Details
This page is intentionally tool-agnostic and focuses on method quality, workflow evidence, and decision clarity.