Aglet

Prioritize Discovery Assumptions by Learning Value

Not every assumption deserves equal research time. Prioritization asks which unproven belief could most damage the direction, which is easiest to test, and which answer would unlock a real decision. Use evidence quality and reversibility instead of enthusiasm as the ranking basis.

Decide where the work belongs

  1. Map consequence and uncertainty

    For each belief, describe the user or business outcome if it is false, then record how much direct evidence supports it. Separate high-consequence uncertainty from low-consequence speculation. Include assumptions about access, adoption, workflow fit, and delivery constraints.

  2. Compare learning paths

    List the smallest research activity that could test each high-risk belief: observation, interview, prototype, diary prompt, or bounded experiment. Compare time, participant access, interpretation risk, and how quickly the result would change a decision.

  3. Make a queue decision

    Choose a top assumption, a next test, an owner, and a review date. Defer lower-ranked bets with a visible reason and trigger. If two beliefs depend on one another, test the dependency first rather than averaging their scores.

What to carry forward

The priority output is an ordered assumption queue with consequence, confidence, test cost, owner, and revisit trigger. Select the bet whose answer could change the direction soonest. Treat the ranking as provisional when consequence or evidence quality is still poorly understood.

Technical background: GOV.UK user research guidance.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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