The DOI matches its DataCite record; 1 of the 1 references it cites were found in a registry or an index.
The power of outliers (and why researchers should ALWAYS check for them)
Abstract
There has been much debate in the literature regarding what to do with extreme or influential data points. The goal of this paper is to summarize the various potential causes of extreme scores in a data set (e.g., data recording or entry errors, motivated mis-reporting, sampling errors, and legitimate sampling), how to detect them, and whether they should be removed or not. Another goal of this paper was to explore how significantly a small proportion of outliers can affect even simple analyses. The examples show a strong beneficial effect of removal of extreme scores. Accuracy tended to increase significantly and substantially, and errors of inference tended to drop significantly and substantially once extreme scores were removed
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“we agree with the general wisdom that transformations can mitigate problems with outliers (Osborne & Overbay, 2004), heteroscedasticity (McGuinness, 2002), and sometimes with interpretability of parameter estimates.”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Osborne & Overbay, 2004
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
Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
Cited by
Violating the normality assumption may be the lesser of two evils
Knief, U., & Forstmeier, W. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01587-5 · Cited by 654
Sinn, N., Milte, C. M., Street, S. J., Buckley, J. D., Coates, A. M., Petkov, J., & Howe, P. R. C. (2012) · British Journal of Nutrition · doi:10.1017/s0007114511004788 · Cited by 297
Moieni, M., Irwin, M. R., Jevtic, I., Olmstead, R., Breen, E. C., & Eisenberger, N. I. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.17 · Cited by 273
Consumer Responses to AI-Generated Charitable Giving Ads
Arango, L., Singaraju, S. P., & Niininen, O. (2023) · Journal of Advertising · doi:10.1080/00913367.2023.2183285 · Cited by 243
A Survey of Evaluation Metrics Used for NLG Systems
Sai, A. B., Mohankumar, A. K., & Khapra, M. M. (2023) · ACM Computing Surveys · doi:10.1145/3485766 · Cited by 235
Metadata
- Authors
- Osborne, J. W., & Overbay, A.
- Venue
- University of Massachusetts Amherst
- Publisher
- University of Massachusetts Amherst
- Published
- 2004
- References
- 1
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
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.
