Analytical Methods · 2015 · 59 citations · 1 references
Natural SciencesBiostatisticsMathematicsData MiningMethod ValidationStatisticsData AnalysisHypothesis TestingOutlier DetectionOutlier HandlingRobust StatisticsStatistical AnalysisCochran's Q TestExtreme Value AnalysisOutlier Detection EvaluationOutlier IdentificationOutlier RemovalOutlier ScoringStatistical Outlier DetectionStatistical Outlier Treatment
In a previous Technical Brief (TB No. 39) three approaches for tackling suspect results were summarised. Median-based and robust methods respectively ignore and down-weight measurements at the extremes of a data set, while significance tests can be used to decide if suspect measurements can be rejected as outliers. This last approach is perhaps still the most popular one, and is used in several standards, despite possible drawbacks. Here significance testing for identifying outliers is considered in more detail with the aid of some typical examples.
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