Assumptions

What does “Assumptions” mean in practice?

In Product Thinking, this lesson connects Assumptions to a choice someone genuinely needs to make. Start with a familiar example, then check when Assumptions helps and when the label hides the real problem. After this lesson, you can recognize Assumptions in everyday examples without applying the label too quickly.

After this lesson

• In Product Thinking, this lesson connects Assumptions to a choice someone genuinely needs to make.
• Use the idea of “Assumptions” to interpret one realistic situation.
• Explain the limits of the concept and the information that still needs to be checked.

Start with the situation

Understand the situation first. The label can come later.

A model score can improve without helping users finish their work faster. This lesson uses the idea of “Assumptions” to examine that situation without treating a single term as the answer to every problem.

In Product Thinking, this lesson connects Assumptions to a choice someone genuinely needs to make. Connect assumptions, success measures, safeguards, and decision rules in one test plan. Connect the term to a decision someone genuinely needs to make.

See how the decision unfolds

Move from the situation to a choice others can review.

Do not rush the choice

Two ways to look at Assumptions

The stronger choice is the one whose evidence, owner, and limits can be explained, not simply the more sophisticated option.

Visual model

Map the parts before choosing what to do.

Read the diagram as a map of Assumptions: begin with the context, follow the connections, and inspect the highlighted point before making a decision.

Pause for a moment

What evidence could change this decision?

Answer before opening the discussion. Name one fact and one assumption.

A tempting shortcut

A familiar term can still lead us to the wrong decision.

Why this can seem reasonable

A technical metric may not reflect the outcome users experience. This mistake often appears when a label is used before the problem is understood. Write down your assumptions so another person can review them.

How to check it

Start with a familiar example, then check when Assumptions helps and when the label hides the real problem. Begin with what can be observed, then separate facts, assumptions, and open questions.

Try it on your work

Try it with one small piece of real work.

1. Choose one real situation related to Assumptions.
2. Separate what you can observe from what you are assuming.
3. Write one decision, its owner, and the evidence needed to review it.
4. Name the signal that would make you stop or change direction.

Write two examples that fit Assumptions and one that does not. Explain the difference in your own words. The larger module activity is: Design an experiment with a clear success measure and stop condition. Keep the first version small enough for another person to review in a few minutes.

Quick practice

Write two examples that fit Assumptions and one that does not. Explain the difference in your own words. The larger module activity is: Design an experiment with a clear success measure and stop condition.

Summary

• In Product Thinking, this lesson connects Assumptions to a choice someone genuinely needs to make.
• Use examples and evidence to test your understanding.
• Record the limits, risks, and conditions that should trigger another review.

Continue from here

• Offline and Online Metrics: Continue the idea from Experiments and Metrics with a closely related example.
• Human Review and Fallbacks: Connect this lesson to Applied AI and test the idea in another context.
• Data Leakage and Overfitting: See how the same decision changes when viewed through Data Literacy.

Sources and further reading

• Evaluation best practices: OpenAI · official-documentation. Primary reference for the definition, evidence, or limits discussed in “Assumptions”.
• Model evaluation: scikit-learn · official-documentation. Further evidence and context for checking the explanation in “Assumptions”.
• Classification: Accuracy, recall, precision, and related metrics: Google for Developers · official-course. Further evidence and context for checking the explanation in “Assumptions”.