Formulating Your Hypotheses
Last updated
Last updated
The ability to formulate hypotheses is a critical component of a Product Manager's toolkit. It involves converting observations and assumptions into testable predictions to guide product development. This section will walk through the methodical steps required to create hypotheses that are grounded in data and user insights.
A YouTube Product Manager, who has identified the assumption that users are looking for more structured educational content, now begins the process of formulating a hypothesis. The first step is to translate the assumption into a testable question: "Will creating personalized learning paths increase user session length for educational content?"
The second step involves defining the variables – in this case, 'personalized learning paths' as the independent variable and 'user session length' as the dependent variable. The third step is to construct a predictive statement, which for our PM might be: "Introducing personalized learning paths for educational content will lead to an increase in user session length."
The fourth step is to ensure that the hypothesis is SMART: Specific, Measurable, Achievable, Relevant, and Time-bound. For example: "By introducing personalized learning paths, we aim to increase the average session length by 20% among users who have engaged with educational content in the past month, over a trial period of 60 days."
The final step is to plan for measurement and validation, deciding in advance what data will be collected and how the results will be evaluated. The PM decides to use A/B testing, comparing the session lengths of users exposed to the new feature against those who are not, with a clear definition of what constitutes a statistically significant result.
The challenges in formulating hypotheses include avoiding bias in the hypothesis statement, ensuring that the hypothesis is narrowly focused and testable, and establishing clear criteria for validation. PMs must also be wary of overconfidence in their assumptions and remain open to the possibility that the data may not support their predictions.
Take a feature or user behavior from a product that you believe can be improved. Follow the steps outlined above to formulate a hypothesis. How will you ensure that your hypothesis is SMART? What data will you need to validate your hypothesis, and how will you interpret the results?
Hypothesis Testing [ | ]
Statistical Analysis in Product Management [ | ]
User Experience Research [ | ]
Behavioral Economics [ | ]
Designing Controlled Experiments [ | ]
Interpreting User Data. [ | ]