Everything else is ready: study the parts, the key terms and the quiz below.
- Part 11Name the people the claim concerns
- Part 22Trace selection into observed records
- Part 33Count the interviewed age distribution
- Part 44Put published population totals beside the sample
- Part 55Explain why a row percentage can differ
- Part 66Check the common split mistake
- Part 77Reproduce the comparison and decide
Who Is in the Dataset?
See how the sampling frame and missing responses can make a precise-looking summary unrepresentative.
In this lesson7 parts
- 01Name the people the claim concerns
- 02Trace selection into observed records
- 03Count the interviewed age distribution
- 04Put published population totals beside the sample
- 05Explain why a row percentage can differ
- 06Check the common split mistake
- 07Reproduce the comparison and decide
Key terms
The words this lesson introduces, each in one line. The module’s glossary collects them all.
- target population
- The people the intended population claim concerns.
- control total
- A published population count used here for comparison.
- sampling frame
- The operational set or process from which sample members can be selected.
- nonresponse
- A selected person's requested data are not obtained.
- oversampling
- Selecting a declared group at a higher rate by design.
- age band
- A declared interval of recorded ages.
- survey weight
- A released value used with survey guidance to represent the target population in an analysis.
- missingness mechanism
- A possible reason a value is missing; the rows alone cannot say which one applies. (narration)
- MCAR (missing completely at random)
- Missingness probability independent of observed and unobserved values.
- MAR (missing at random)
- Missingness independent of the missing value after conditioning on observed variables.
- MNAR (missing not at random)
- Missingness still depends on unavailable values after conditioning on observed information.
- random train/test split
- A random partition of the rows already observed.
Quiz 5 questions
Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.
Practice
Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.
The self-checking notebook for this lesson is Who is in the dataset?.
Common mistakes
What you will see when it goes wrong, why it happens, and the fix.
Where it’s used
Where this lesson’s ideas turn up in real work.