Science demo
Turn observations into a testable hypothesis
Move from observation to a claim that could actually be wrong, with falsification stated.
The challenge
Hypotheses get framed so they cannot fail, which makes the experiment uninformative.
The scenario
A set of observations suggests a mechanism, and the team needs a claim worth testing.
What goes in
- Observations
- What was actually observed, with method.
What you can ask
Illustrative prompts. Results depend on your own data and are not deterministic.
- “Turn these observations into a testable hypothesis.”
- “What result would falsify this?”
- “Which of this is assumption rather than observation?”
How Clearception approaches it
How the work is structured. Each step shows what its output is grounded in.
Separate the classes
ObservedDistinguish observation, inference and assumption.
Form the claim
AI-inferredState a hypothesis capable of being wrong.
Define falsification
SourceState what result would disprove it.
What you get
The shape of what comes back — not a promised result.
Testable hypothesis
AI-inferredA claim with explicit falsification criteria and its evidential basis.
Why it matters
- Experiments that inform
- A claim that can fail is one worth testing.
Related demos
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Plan a test capable of returning the result that would change your mind.
4 minute walkthrough
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State what would prove you wrong
Make the conditions for being wrong explicit before the data arrives.
3 minute walkthrough
- Hypothesis Design
- Fact / Inference Separation
Robotics
Plan a task, bounded by the safety plane
Turn a described goal into a task plan that the deterministic safety plane must accept before it runs.
4 minute walkthrough
- Deterministic Safety Plane
- Deterministic Policy Engine
- Evidence Trail
Want to try this with your own work?
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