Graphs in Statistical Analysis¶
Anscombe, F. J. (1973). Graphs in Statistical Analysis. The American Statistician, 27(1), 17-21.
Cited by¶
9 citations across 9 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Calibration-Curve Residual Report
- A correlation coefficient rewards a curve for being tight, not for being unbiased, so a systematically curved response can post a near-perfect R² while quietly mis-reading a whole region.
This sourceAnscombe’s quartet shows that similar correlation and fitted-regression summaries can conceal curved structure and consequential regional misfit.
- A correlation coefficient rewards a curve for being tight, not for being unbiased, so a systematically curved response can post a near-perfect R² while quietly mis-reading a whole region.
- Correlation Heatmap
- Identical correlation values can hide wildly different shapes.
This sourceAnscombe's four datasets demonstrate that identical correlation values and regression summaries can coexist with radically different plotted shapes.
- Identical correlation values can hide wildly different shapes.
- Diagnostic Plot Review
- Anscombe's quartet — four datasets with identical means, variances, and regression lines but wildly different shapes — is the standing proof that you cannot trust summary statistics without looking at the picture.
This sourceConstructs four datasets with the same principal summary and regression statistics but very different graphical patterns, demonstrating the need to plot the data.
- Anscombe's quartet — four datasets with identical means, variances, and regression lines but wildly different shapes — is the standing proof that you cannot trust summary statistics without looking at the picture.
- Distributional Dashboard
- A subgroup no panel breaks out is exactly as hidden as it was under a bare average, and a distribution that looks healthy on the displayed axes can still hide harm on an axis nobody chose — two views can share identical summary statistics yet describe utterly different distributions.
This sourceDemonstrates that data sets with nearly identical summary statistics can have radically different distributions.
- A subgroup no panel breaks out is exactly as hidden as it was under a bare average, and a distribution that looks healthy on the displayed axes can still hide harm on an axis nobody chose — two views can share identical summary statistics yet describe utterly different distributions.
- Joint-Distribution Diagnostic Panel
- Its strength is that it catches the failures a coefficient is structurally blind to: the Anscombe trap of identical summaries over different shapes, data-entry outliers, mixed subpopulations, and join errors that silently corrupt the pairing.
This sourceDemonstrates that datasets with virtually identical conventional summaries can have sharply different graphical forms, including a case dominated by an outlying observation.
- Its strength is that it catches the failures a coefficient is structurally blind to: the Anscombe trap of identical summaries over different shapes, data-entry outliers, mixed subpopulations, and join errors that silently corrupt the pairing.
- Residual Divergence Map
- Anscombe's quartet
This sourceUses Anscombe's quartet to show that nearly identical summary statistics can conceal sharply different data structures.
- Anscombe's quartet
- Residual-versus-Fitted Plot
- Its strength is speed and directness: it is the fastest way to catch a misspecified mean function, and a single picture surfaces curvature
This sourceShows that a plot can surface curvature that a lone fit statistic hides.
- Its strength is speed and directness: it is the fastest way to catch a misspecified mean function, and a single picture surfaces curvature
- Summary Statistics
- Worse, radically different distributions can share identical summary statistics — the point of Anscombe's quartet, four datasets with the same mean, variance, and correlation but wholly different shapes.
This sourceConstructs four datasets with matching standard numerical summaries—including means, variances, and correlation—whose plotted forms are radically different.
- Worse, radically different distributions can share identical summary statistics — the point of Anscombe's quartet, four datasets with the same mean, variance, and correlation but wholly different shapes.
- Support, Shape, and Tail Diagnostic Suite
- The cautionary classic is Anscombe's quartet — four datasets with identical means, variances, and correlations but wildly different shapes — which is exactly why a responsible profile always looks at multiple views rather than trusting the summary numbers.
This sourceConstructs four datasets with matching summaries but sharply different plots to show why graphical views must accompany statistics.
- The cautionary classic is Anscombe's quartet — four datasets with identical means, variances, and correlations but wildly different shapes — which is exactly why a responsible profile always looks at multiple views rather than trusting the summary numbers.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Not recorded. Neither this nor any other of the 9 citations of this work carries a recorded support check.
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