Power-Law Distributions in Empirical Data¶
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-Law Distributions in Empirical Data. SIAM Review, 51(4), 661-703.
Cited by¶
6 citations across 6 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Fractal Geometry
- Are the apparent power laws in the data robust to rigorous statistical testing
This sourceMaximum-likelihood-plus-KS-plus-likelihood-ratio framework for rigorously identifying power-law distributions; showed many previously-claimed power laws fail proper goodness-of-fit testing (bibliography + inline; supports the T3 power-law-over-fitting failure mode and the Abstract Reasoning / Notes cautions).
- Are the apparent power laws in the data robust to rigorous statistical testing
- Scale Invariance
- - T1 — Claimed Power Laws Are Often Not Power Laws: Visual inspection of log-log plots can deceive; formal statistical testing
This sourceComprehensive guide to statistical methods for identifying and testing power-law hypotheses; establishes rigorous standards for power-law claims.
- - T1 — Claimed Power Laws Are Often Not Power Laws: Visual inspection of log-log plots can deceive; formal statistical testing
Domain-specific¶
Mechanisms¶
- Finite-Size Scaling Check
- Instead, the group finds the apparent slope drifts systematically with catalog size and the tail is dominated by a handful of events in one short, unusually active window — the "power law" is largely a finite-sample artifact.
This sourceShows that finite samples bias estimated power-law exponents and can make non-power-law data appear to fit a power law.
- Instead, the group finds the apparent slope drifts systematically with catalog size and the tail is dominated by a handful of events in one short, unusually active window — the "power law" is largely a finite-sample artifact.
- Log-Log Scaling Plot
- Its notorious failure is the spurious power law
This sourceShows that non-power-law distributions can look roughly straight on log-log axes, so a visual proclamation of a power law must be subjected to formal goodness-of-fit testing that can reject it.
- Its notorious failure is the spurious power law
- Log-Log Survival Plot
- The discipline is to use the plot as a gate and a prompt, never a verdict: confirm heaviness with formal tail-index estimation and goodness-of-fit, and keep the actionable claim modest — "heavy-tailed, so averages are unsafe," not "exactly a power law."
This sourceTreats plots as preliminary diagnostics and recommends parameter estimation, goodness-of-fit testing, and comparisons with alternative heavy-tailed models before claiming a power law.
- The discipline is to use the plot as a gate and a prompt, never a verdict: confirm heaviness with formal tail-index estimation and goodness-of-fit, and keep the actionable claim modest — "heavy-tailed, so averages are unsafe," not "exactly a power law."
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 6 citations of this work carries a recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:5cc1aa9f3d30 · see in the full table