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Misuse of statistics
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===Data manipulation=== {{distinguish | text=[[Data processing]], [[Data preparation]], or [[Data wrangling]], overlapping terms which are often referred to generally as "data manipulation"}} Informally called "fudging the data," this practice includes selective reporting (see also [[publication bias]]) and even simply making up false data. Examples of selective reporting abound. The easiest and most common examples involve choosing a group of results that follow a pattern [[consistent]] with the preferred [[hypothesis]] while ignoring other results or "data runs" that contradict the hypothesis. Scientists, in general, question the validity of study results that cannot be reproduced by other investigators. However, some scientists refuse to publish their data and methods.<ref>{{cite journal|last=Neylon |first=C |year=2009 |title=Scientists lead the push for open data sharing |journal=Research Information |publisher=Europa Science |volume=41 |pages=22β23 |url=http://www.researchinformation.info/features/feature.php?feature_id=214 |issn=1744-8026 |url-status=unfit |archive-url=https://web.archive.org/web/20131203050247/http://www.researchinformation.info/features/feature.php?feature_id=214 |archive-date=December 3, 2013 }}</ref> Data manipulation is a serious issue/consideration in the most honest of statistical analyses. Outliers, missing data and non-normality can all adversely affect the validity of statistical analysis. It is appropriate to study the data and repair real problems before analysis begins. "[I]n any scatter diagram there will be some points more or less detached from the main part of the cloud: these points should be rejected only for cause."<ref name=fpp3>{{harvnb| Freedman | Pisani | Purves | 1998 |loc=chapter 9: More about correlations, Β§3: Some exceptional cases}}</ref>
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