Distinguish association, cause, and kinds of change
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
Headlines can make a modest absolute difference sound large by reporting only a relative change. The arithmetic may be valid while the causal wording remains unsupported. You will calculate both forms and examine a fictional reminder-app comparison in which the groups and course schedules differ. The aim is to describe the evidence precisely and identify a stronger design.
A change in numbers does not by itself identify what caused the change.
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 both fictional group records and calculate the late-assignment share for each. Identify assignments as the denominator and note that the cohorts contain different volunteers.
- 2
Calculate the absolute change in percentage points and the relative decrease using the original 20% as the baseline. Label the two units so they cannot be confused.
- 3
List at least two alternative explanations, including group differences and course schedules. Explain how each could affect lateness independently of the reminder app.
- 4
Rewrite the headline as a descriptive comparison that does not attribute the whole change to the app. Keep the useful numerical result while narrowing the causal claim.
- 5
Describe a stronger comparison, such as comparable participants randomly assigned where appropriate with similar measurement and follow-up. State that randomization supports causal inference under conditions rather than guaranteeing perfectly identical groups.
See how it works.
Fictional scenario: In one term, twenty volunteer learners submit 100 assignments and twenty are late. In a later term, twenty different volunteers using a reminder app submit 100 assignments and ten are late. Late shares fall from 20% to 10%: a ten-percentage-point absolute decrease and a 50% relative decrease from the baseline. The headline says the app caused late work to halve. Noor notes that participants and course schedules changed, so the comparison cannot isolate the app. The observed difference is real within the supplied records, but its cause needs better evidence.
Did you cover these?
- Late shares are 20% and 10%.
- Absolute decrease is ten percentage points; relative decrease is 50%.
- Two confounds accompany the revised headline.
What to finish
A worksheet comparing percentage-point and relative changes, a list of other possible explanations, and a headline that keeps the cause uncertain.
Watch for this
Using a before-and-after difference as proof of causation or confusing a percentage-point decrease with a relative percent decrease.
How can I tell I understand it?
You compute both kinds of change and identify why the study design cannot isolate the proposed cause.
Quick knowledge check
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Source & access notes · checked 2026-09-10
Opened design, response/explanatory variables, and lurking-variable discussion. Course language avoids the source’s overly absolute phrasing about randomization guaranteeing equal groups or proving causation.
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