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Assessment: Distinguishing Between Probability & Non-Probability Samples :Understanding and Visualizing Data with Python (Statistics with Python Specialization) Answers 2025

1. Question 1

A random sample of U.S. households is selected from a population address list, with known different sampling rates (oversampling low-income).

  • ✅ Probability

  • ❌ Non-Probability

Explanation:
Selection is random from a known frame and each unit’s selection probability is known (even though unequal). That is the definition of a probability sample (weights can correct for oversampling).

2. Question 2

Random digit dialing (RDD) from landline and mobile lists with known, different sampling rates.

  • ✅ Probability

  • ❌ Non-Probability

Explanation:
RDD is random selection from (generated) telephone frames and the sampling rates are known — so this is a probability sample (unequal probabilities are still probability sampling).

3. Question 3

Standing on a busy campus corner and asking passersby until 100 people are interviewed.

  • ❌ Probability

  • ✅ Non-Probability

Explanation:
This is convenience sampling (self-selection of whoever passes by and agrees). There’s no random selection from the campus population frame.

4. Question 4

Multistage design: PPS selection of counties, random housing units within counties, random adult within household.

  • ✅ Probability

  • ❌ Non-Probability

Explanation:
Every stage uses random selection from defined frames and selection probabilities are known, so this is a probability (complex/clustered) sample.

5. Question 5

After completing the survey in Q4, respondents are invited to join a web panel for future surveys.

  • ❌ Probability

  • ✅ Non-Probability

Explanation:
Joining the web panel is voluntary/self-selected; membership is not randomly assigned from the population — leads to a non-probability panel.

6. Question 6

Visitors click an ad on a sports site saying they’ll be paid for opinions and then participate.

  • ❌ Probability

  • ✅ Non-Probability

Explanation:
This is voluntary/self-selected participation (convenience/self-selection) — not random sampling from the site’s user population.

7. Question 7

Researcher recruits at a homeless shelter and asks those respondents to tell their social networks about the opportunity (chain referrals).

  • ❌ Probability

  • ✅ Non-Probability

Explanation:
This is snowball / chain-referral sampling — selection depends on social networks and is not random from a complete sampling frame.

8. Question 8

Professor actively recruits volunteers until she has exactly 100 males and 100 females, then stops.

  • ❌ Probability

  • ✅ Non-Probability

Explanation:
This is quota sampling with volunteers. Recruitment is nonrandom (volunteers) and stopping at quotas creates a non-probability sample.

9. Question 9

Facebook samples a random sample of 100,000 posts from identified users in D.C. and analyzes content to estimate proportion of users who tweeted about Trump.

  • ❌ Probability

  • ✅ Non-Probability

Explanation:
Although the selection of posts is random, the target population is users. Sampling posts gives unequal probabilities to users (active posters more likely to be sampled), so this does not constitute a proper probability sample of users for estimating the proportion of users. The sampling frame/unit mismatch (posts vs users) induces selection bias for the user-level estimate — thus it should be treated as non-probability for that user-level estimate unless additional design/weights correct for per-user selection probabilities.

10. Question 10

University randomly selects 2,500 males and 2,500 females from registrar lists.

  • ✅ Probability

  • ❌ Non-Probability

Explanation:
Selection is random from the registrar frame within sex strata; known selection probabilities (stratified random sampling). That is a probability sample.

🧾 Summary Table

Q# Correct Answer Key concept
1 Probability ✅ Random selection from frame; known probabilities (unequal allowed).
2 Probability ✅ RDD with known sampling rates → probability sampling.
3 Non-Probability ✅ Convenience / passersby — no random selection.
4 Probability ✅ Multistage random selection (PPS, random HU, random adult).
5 Non-Probability ✅ Voluntary panel enrollment → self-selection.
6 Non-Probability ✅ Ad clickers self-select → convenience sample.
7 Non-Probability ✅ Snowball / chain-referral sampling — not random.
8 Non-Probability ✅ Quota from volunteers — nonrandom recruitment.
9 Non-Probability ✅ Random posts, not users — unit/frame mismatch → biased for user-level estimate.
10 Probability ✅ Stratified random sample from registrar lists.