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3.4 · Paper 1 · Compulsory
Spotting misleading graphs, dodgy statistics and flawed models — the 'thinking' unit that ties the whole course together.
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When judging whether a conclusion is justified, check: is the sample large enough and representative? Is there a plausible confounding variable that could explain a correlation without one thing causing the other? Has the evidence actually been interpreted correctly, or has the argument overstated what the data shows?
Common flawed reasoning to watch for: assuming correlation implies causation; drawing big conclusions from a small or biased sample; and cherry-picking data that supports a conclusion while ignoring data that doesn't.
Graphs can mislead through: a truncated y-axis (not starting at zero) that exaggerates small differences; inconsistent scales; cherry-picked time windows that hide a longer-term trend; or using the wrong average (e.g. quoting the mean when a few extreme values distort it, when the median would be more representative).
Every model is a simplification, built on assumptions — a good critical answer identifies what those assumptions are and whether they're reasonable for the situation. A model might work well within the range of data it was built from (interpolation) but fail badly outside that range (extrapolation), or it might ignore real-world factors that matter (e.g. a linear population growth model that ignores resource limits).
A news article states: "Ice cream sales and shark attacks both rise in summer, proving ice cream sales cause shark attacks."
Critically evaluate this claim.
This is a classic correlation-does-not-imply-causation error. Both ice cream sales and shark attacks rise in summer because of a confounding variable: warmer weather, which leads to more people buying ice cream and more people swimming in the sea (increasing shark encounters). There's no plausible mechanism by which eating ice cream would cause shark attacks, so the claimed causal link is not justified by this correlation.
Practice