Quality Standards
Purpose: Define minimum acceptable quality for cycle outputs.
Quality standards exist to help teams move faster with less rework. They are not there to make the process heavier. They are there to stop weak work from quietly creating downstream confusion.
Assumption quality
Specific, not vague.
Directly tied to value, behavior, or feasibility.
Prioritized by risk and impact.
Good assumption quality means the team can point to one meaningful uncertainty and explain why it matters now.
Hypothesis quality
Testable in a finite period.
Contains measurable expected outcome.
Linked to one core assumption.
A strong hypothesis makes it obvious what the team expects to observe if the assumption appears true.
Experiment quality
Clear method and success criteria.
Feasible within team constraints.
Produces interpretable evidence.
An experiment is high quality when it helps the team decide something. If it only produces interesting information without affecting confidence or action, it is probably weaker than it looks.
Learning quality
Derived from evidence, not interpretation alone.
Includes confidence and uncertainty.
Clearly states what changed in team understanding.
Good learnings are humble and useful. They do not overclaim. They help the team understand what shifted, how strongly, and what still remains unclear.
Insight quality
Synthesizes multiple learnings where relevant.
Includes decision implications.
Supports a specific next action.
A strong insight helps the team move from “Here is what happened” to “Here is what this means.”
Why these quality bars matter
Low-quality work often feels fine in the moment. The problem shows up later:
vague assumptions create messy tests,
messy tests create weak learnings,
weak learnings create hesitant decisions,
hesitant decisions slow the whole system down.
That is why quality should be checked early, not just at the end.
Weak vs strong pattern
Assumption
Broad and hard to test
Specific, risky, and worth testing now
Hypothesis
Hard to measure
Measurable and time-bounded
Experiment
Busy but unclear
Explicitly tied to a decision
Learning
Sounds like an opinion
Evidence-backed and confidence-tagged
Insight
Interesting but passive
Makes the next move clearer
Practical use
Use these standards during the cycle, not after it. The best time to improve quality is before weak work compounds into a slower decision cycle.
Next step
Read Common Failure Patterns.
Last updated