Take apart a number someone is about to act on
Find a data-backed claim that is currently being used to justify a decision — from your own organisation, a public report, or a news story with the underlying figures published. Write the critique you would actually send. The test of this piece is not whether you can find something wrong; almost any claim has something wrong. It is whether you can find the *one* thing that matters and propose something that would settle it.
What you have to hand in
- The claim quoted exactly, with its source.
- The population the figure describes, and the population the claim is about.
- One assumption named as load-bearing, with the reason it carries the weight.
- Weaker objections explicitly set aside rather than listed.
- A specific, obtainable test, and what result would change your own mind.
- Written so the person who made the claim would read it to the end.
The checklist, published before you start
- The load-bearing assumption is found
The critique identifies the one assumption the claim actually rests on, rather than listing every possible objection.
- No assumption identified, or the critique attacks the conclusion directly.
- Several objections listed with no view on which matters most.
- One assumption named as load-bearing, with a reason.
- Named, reasoned, and the weaker objections explicitly set aside.
- Something that would settle it
A concrete piece of evidence or a specific analysis that would confirm or kill the claim.
- No test proposed, or "we need more data".
- A direction is suggested but not something anyone could act on.
- A specific, obtainable test with a stated expected outcome.
- Specific, obtainable, and says what result would change the author's own mind.
- The population is named
The submission says precisely which group the figure describes, and which group the claim is about.
- Treats the sample as the population without noticing.
- Mentions the sample but not the gap to the population of interest.
- Names both groups and the gap between them.
- Names both, and says which direction the gap pushes the result.
- The critique is proportionate
The response matches the stakes and stays usable by the person who made the claim.
- Dismissive, or so hedged it makes no point.
- Correct but adversarial, or buried in caveats.
- Direct, specific, and collaborative.
- Reads as help rather than correction, without softening the point.
The skills a pass would prove
- Challenging a claimTake a data-backed claim apart into its assumptions and name which one is load-bearing
- Population and sampleState precisely which population a figure supports a claim about, and which it does not
- Sampling biasIdentify who is missing from a dataset and say which direction their absence pushes the result
- Correlation and causeName a plausible confounder for a causal claim and say what evidence would rule it out
This brief is part of the Statistics & Data Literacy course
Starting it starts the course: every skill above, in the order they depend on each other, with this brief at the end as the thing you hand in — marked against the checklist you have just read and nothing else.
Start the Statistics & Data Literacy course