Hypothesis thinking ensures that decisions are objective, deliberate, data-driven, and focused on continuous learning and improvement.
This practice enables a team to turn assumptions into a testable format - a hypothesis. Its fundamental premise is that most beliefs or points of view gathered as part of ideation or discovery are opinions, however well formed.
By using a formal construct to articulate a belief, most of the subjectivity surrounding decisions is removed by capturing data from the associated experiment.
The typical steps in this practice are: Formulating the Hypothesis: Once the problem/assumtpion is identified, the product manager formulates a clear hypothesis followed by the experiment design. This could involve creating a prototype, introducing a new feature behind a feature flag, or rolling out a minimal viable product (MVP).
🔑 Key Benefits / Why is this important
Risk Reduction by focusing on small bet experiments informed by the hypothesis, before converging on a chosen solution with larger investment and effort
Data-Driven Decision emphasizes evidence over intuition or mere guesswork, ensuring decisions are grounded in actual user behaviour and feedback.
Aligns well with agile methodologies, emphasizing quick iterations, feedback loops, and adaptability.
Accelerated learning to validate key assumptions. Even invalidated hypotheses provide valuable insights. By understanding what doesn't work, or what users don't want, teams can refine their understanding of their users and the market.
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