Statistical critique

Objective. Assess graphs, samples and conclusions for misleading features.

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Learn: the key idea

Statistical critique evaluates whether graphs, sampling methods and conclusions genuinely support the claims made, spotting misleading scales, biased samples or unjustified causation claims. Check axis scales for truncation or non-uniform intervals, check whether a sample is representative of the whole population, and check whether correlation is wrongly presented as causation. A bar chart starting its vertical axis at 90 instead of 0 can make a small difference look dramatic. When critiquing, always name the specific flaw and explain its effect on the reader's interpretation, rather than giving a vague complaint.

Worked example

A newspaper's bar chart comparing two shops' sales has a vertical axis starting at 80 rather than 0. Explain the main misleading feature of this critique scenario and its effect.

1 $Identify the truncated (non-zero) vertical axis$
2 $This exaggerates small differences between bar heights visually$
3 $The reader perceives a much larger gap than the true values show$
4 $State: the axis should start at 0 to represent sales fairly$

Answer: The non-zero axis exaggerates the difference between the shops' sales

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Common mistake. A weak critique simply says a graph or survey is 'biased' without identifying the actual flaw, such as a non-zero axis or an unrepresentative sample; examiners require the specific mechanism to be stated.

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