Who Was Asked: Samples and the People Left Out
A statistic is about the people who answered, not the people you care about. Covers the difference between a population and a sample, why the method of asking decides who is missing, and how a poll of a self-selected group can be precisely wrong.
What a learner can do afterwards
- State the population a given claim is really about, given how the data was collected
- Name two ways of choosing a sample that leave out a predictable group, and say who is missing in each
- Explain why a larger self-selected sample does not fix a biased one
1 · Read
A statistic describes the people who answered, not the people you care about. The population is everyone the claim is about. The sample is the people actually measured. A website poll of its own readers speaks about those readers, never the whole public.
The method of asking decides who is missing. Landline calls in work hours miss mobile-only homes and workers. A survey at one station at noon misses everyone elsewhere. Convenience and volunteer samples favour whoever is easiest to reach.
A bigger self-selected sample never fixes the tilt. Ten thousand extra clicks still exclude everyone offline. Bias is built into the method, while the normal wobble between fair samples is just variation.
Meet every headline with three questions: who was asked, who could not answer, and who chose to stay quiet. If any answer narrows the group, shrink the claim to match.
Ask who answered, who was shut out, and whether a bigger count would only measure the wrong group more precisely.
2 · Watch
Take it off screen
Where it sits
This opens up
Nothing builds on it yet.
Where this leads
Jobs that lean on this skill. Follow one to see everything it is built on.
8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.