Editing Outlier (section)

=== Exclusion ===
When deciding whether to remove an outlier, the cause has to be considered.  As mentioned earlier, if the outlier's origin can be attributed to an experimental error, or if it can be otherwise determined that the outlying data point is erroneous, it is generally recommended to remove it.<ref name="karch2023"/><ref name="bakker2014">{{cite journal |last1=Bakker |first1=Marjan |last2=Wicherts |first2=Jelte M. |title=Outlier removal, sum scores, and the inflation of the type I error rate in independent samples t tests: The power of alternatives and recommendations. |journal=Psychological Methods |date=2014 |volume=19 |issue=3 |pages=409–427 |doi=10.1037/met0000014|pmid=24773354 }}</ref> However, it is more desirable to correct the erroneous value, if possible. 

Removing a data point solely because it is an outlier, on the other hand,  is a controversial practice, often frowned upon by many scientists and science instructors, as it typically invalidates statistical results.<ref name="karch2023"/><ref name="bakker2014"/> While mathematical criteria provide an objective and quantitative method for data rejection, they do not make the practice more scientifically or methodologically sound, especially in small sets or where a normal distribution cannot be assumed. Rejection of outliers is more acceptable in areas of practice where the underlying model of the process being measured and the usual distribution of measurement error are confidently known. 

The two common approaches to exclude outliers are [[truncation (statistics)|truncation]] (or trimming) and [[Winsorising]]. Trimming discards the outliers whereas Winsorising replaces the outliers with the nearest "nonsuspect" data.<ref>{{cite book |title=Data Analysis: A Statistical Primer for Psychology Students |pages=24–25 |first=Edward L. |last=Wike |date=2006 |publisher=Transaction Publishers |isbn=9780202365350}}</ref> Exclusion can also be a consequence of the measurement process, such as when an experiment is not entirely capable of measuring such extreme values, resulting in [[censoring (statistics)|censored]] data.<ref>{{cite journal |title=Simplified estimation from censored normal samples |first=W. J. |last=Dixon |journal=The Annals of Mathematical Statistics |volume=31 |number=2 |date=June 1960 |pages=385–391 |url=http://projecteuclid.org/download/pdf_1/euclid.aoms/1177705900 |doi=10.1214/aoms/1177705900|doi-access=free }}</ref>

In [[Regression analysis|regression]] problems, an alternative approach may be to only exclude points which exhibit a large degree of influence on the estimated coefficients, using a measure such as [[Cook's distance]].<ref>Cook, R. Dennis (Feb 1977). "Detection of Influential Observations in Linear Regression". Technometrics (American Statistical Association) 19 (1): 15–18.</ref>

If a data point (or points) is excluded from the [[data analysis]], this should be clearly stated on any subsequent report.