Questions and Metrics

What does “Questions and Metrics” mean in practice?

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

After this lesson

• In Data Literacy, this lesson connects Questions and Metrics to a choice someone genuinely needs to make.
• Use the idea of “Questions and Metrics” 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.

Clicks can rise while the user's underlying need remains unresolved. This lesson uses the idea of “Questions and Metrics” to examine that situation without treating a single term as the answer to every problem.

In Data Literacy, this lesson connects Questions and Metrics to a choice someone genuinely needs to make. Turn a broad question into a precise data question, then define the unit, quality, and source of the data. Connect the term to a decision someone genuinely needs to make.

Visual model

Map the parts before choosing what to do.

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

Worked example

Follow the evidence one step at a time.

Diketahui

• Clicks can rise while the user's underlying need remains unresolved. This lesson uses the idea of “Questions and Metrics” to examine that situation without treating a single term as the answer to every problem.
• The situation and available evidence are incomplete.

1. 01 · Frame State the decision that Questions and Metrics is meant to support.
2. 02 · Separate List the observed evidence, assumptions, and missing information.
3. 03 · Compare Compare the likely benefit with the cost of being wrong.
4. 04 · Review Choose a next step and define when it must be reviewed.

Hasil: A conditional decision with an explicit next check.

Interpretasi: The result is useful because it records uncertainty and a review trigger instead of pretending the evidence is final.

Let’s see how it works

Reading the situation in practice

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

Clicks can rise while the user's underlying need remains unresolved. Identify the part of the situation most closely connected to the idea of “Questions and Metrics”. Use the case as a thinking tool, not as proof that one solution fits every context.

A tempting shortcut

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

Why this can seem reasonable

A metric represents part of reality. It is not reality itself. 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 Questions and Metrics helps and when the label hides the real problem. Begin with what can be observed, then separate facts, assumptions, and open questions.

Pause for a moment

What evidence could change this decision?

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

Try it on your work

Try it with one small piece of real work.

1. Choose one real situation related to Questions and Metrics.
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 Questions and Metrics and one that does not. Explain the difference in your own words. The larger module activity is: Audit a small dataset and write down its limits. Keep the first version small enough for another person to review in a few minutes.

Quick practice

Write two examples that fit Questions and Metrics and one that does not. Explain the difference in your own words. The larger module activity is: Audit a small dataset and write down its limits.

Summary

• In Data Literacy, this lesson connects Questions and Metrics 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

Narrative Intelligence

• Unit of Analysis: Continue the idea from Asking Questions with Data with a closely related example.
• What Counts as AI?: Connect this lesson to AI for Everyone and test the idea in another context.
• Users and Jobs: See how the same decision changes when viewed through Product Thinking.

Sources and further reading

• Data on the Web Best Practices: W3C · web-standard. Primary reference for the definition, evidence, or limits discussed in “Questions and Metrics”.
• Machine Learning Crash Course: Google for Developers · official-course. Further evidence and context for checking the explanation in “Questions and Metrics”.
• NIST/SEMATECH e-Handbook of Statistical Methods: NIST · official-handbook. Further evidence and context for checking the explanation in “Questions and Metrics”.