Sort raw observations, summaries, and interpretations so you can tell what evidence you actually have. Practice spotting when a claim says more than the data can support.

Rewrite everyday curiosities as questions that can be answered with observations. Pin down what would need to vary, be counted, or be measured before analysis can begin.

Apply the previous explanations in a guided problem.

Decide what each row of data should represent: a person, day, class, product, visit, or other case. Compare possible choices and see how changing the case changes what the data can answer.

Turn vague ideas like “healthy,” “popular,” or “expensive” into variables with recordable values. Add measurement units, time frames, and clear rules so two people would collect the same kind of data.

Apply the previous explanations in a guided problem.

Check your understanding with a short quiz.

Build the first table for a question by placing cases in rows, variables in columns, and values in cells. Use simple identifiers and clear column names without drifting into summaries or conclusions.

Check whether a proposed dataset really matches the question or claim. Look for missing variables, wrong cases, unclear units, and measurements that would not answer what was asked.

Apply the previous explanations in a guided problem.

Check your understanding with a short quiz.

Review this chapter with practice based on your mistakes.