The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Measuring Skewness and Kurtosis
Abstract
The question of how to measure the degree of skewness of a continuous random variable is addressed. In van Zwet (1964) a method for ordering two distributions with regard to skewness is given. Here, using the concept of comparative skewness, we consider properties that a measure of skewness should satisfy. Several extensions of the Bowley measure of skewness taking values on (-1, 1) are discussed. How well these measures reflect one's intuitive idea of skewness is examined. These measures of skewness are extended to measures of kurtosis for symmetric distributions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Advanced Theory of Statistics.
Clarke, G. M., Kendall, M., & Stuart, A. (1978) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344782 · Cited by 6,658
Cited by
On the meaning and use of kurtosis.
DeCarlo, L. T. (1997) · Psychological Methods · doi:10.1037//1082-989x.2.3.292 · Cited by 1,755
A Quantile Alternative for Kurtosis
Moors, J. J. A. (1988) · The Statistician · doi:10.2307/2348376 · Cited by 630
On more robust estimation of skewness and kurtosis
Kim, T.-H., & White, H. (2004) · Finance Research Letters · doi:10.1016/s1544-6123(03)00003-5 · Cited by 522
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3159580 · Cited by 494
Tests for Normality and Measures of Skewness and Kurtosis: Their Place in Research Reporting
Hopkins, K. D., & Weeks, D. L. (1990) · Educational and Psychological Measurement · doi:10.1177/0013164490504001 · Cited by 324
Metadata
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measuring Skewness and Kurtosis. https://app.cochat.ai/papers/10.2307/2987742
- The paper
- Groeneveld, R. A., & Meeden, G. (1984). Measuring Skewness and Kurtosis. The Statistician. https://doi.org/10.2307/2987742
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
