Identify who and what the numbers describe
See this course’s project requirements
Audit the supplied fictional claim about a reminder app using sampling, summary, conditional-rate, uncertainty, and causal-reasoning tools.
Keep your working files in your own notebook or software. Save a private description, checklist, and work link in My Proof.
What to include
- Identify the claim, population, sample, unit of observation, and sampling limits.
- Calculate and compare mean, median, absolute change, and relative change where appropriate.
- Use a natural-frequency table to compute a conditional percentage with the correct denominator.
- Interpret a supplied confidence interval without treating it as individual variation or certainty.
- Write a claim verdict that separates supported description from unsupported causation and names needed evidence.
Review and revise
Use these criteria to check your work and choose what to improve.
- Calculation: quantities and denominators are correct and reproducible.
- Scope: sample and population claims are distinguished.
- Uncertainty: intervals and study limits are explained accurately.
- Judgment: the conclusion is proportional to the evidence and identifies a useful next question.
Want more background?
The idea in everyday life
Statistics often arrive as a polished headline with the selection process hidden. Before debating the result, identify who or what was actually counted and how those observations entered the dataset. A large or precise-looking number does not automatically represent the intended population. This lesson builds the claim map that will guide your final audit.
Always ask who was counted. A percentage about the people surveyed does not automatically describe everyone.
One step at a time.
Use the sample or information you have permission to use. Keep your work as you go. You can pause between steps.
- 1
Copy the fictional headline and identify its population claim. Then list the actual sample, sample size, question asked, and how respondents were selected.
- 2
Calculate 18 divided by 20 as a percentage. Keep the arithmetic judgment separate from whether the sample represents all learners in the headline.
- 3
Name at least two groups whose views may be missed by the selection method, such as learners unavailable in the evening or those not choosing the workshop.
- 4
Rewrite the headline as “18 of 20 evening-workshop respondents want more evening sessions.” Add a sentence identifying both the unresolved all-learner claim and the unasked comparison with other instructional times.
- 5
Propose a better way to seek the wider population’s views, including a method that reaches absent learners. Save the claim map, corrected wording, and sampling question for the audit brief.
See how it works.
Fictional scenario: A training center asks people leaving an optional evening workshop whether they want more evening sessions. Eighteen of twenty respondents say yes, producing 90%. A headline says “90% of all learners prefer evening instruction.” Ren identifies the population as all center learners, the sample as twenty workshop respondents, and the selection process as a convenience opportunity among people already able to attend evenings. The 90% describes respondents wanting more sessions; it neither establishes preference over other times nor represents all learners. Ren also distinguishes a request for more sessions from evidence that evening sessions improve learning.
Did you cover these?
- The respondent percentage is 90%.
- The sample is identified as evening-workshop respondents.
- The revised headline describes wanting more evening sessions among respondents; it does not claim all-learner preference.
What to finish
A claim-and-sample map, recomputed percentage, and a scope-corrected headline.
Watch for this
Assuming correct arithmetic makes a sample representative or proves an outcome the survey never measured.
How can I tell I understand it?
You distinguish the counted group from the claimed population and explain a selection concern.
Quick knowledge check
Answer all 3 questions. Get at least 2 right and the lesson completes automatically.
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Answer all 3 questions above and get at least 2 right. You can review the explanations and try again.
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Source & access notes · checked 2026-09-10
Opened sampling methods, data types, and variation discussion. Supports identifying sample design and scope; no book examples are reused.
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