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An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. [ 2 ] Anomalies are instances or collections of data that occur very rarely in the data set and whose features differ significantly from most of the data.
The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.
[7] [8] [9] If the data contains potential outliers, if the population distributions have heavy tails, or if the population distributions are significantly skewed, the Kruskal-Wallis test is more powerful at detecting differences among treatments than ANOVA F-test. On the other hand, if the population distributions are normal or are light ...
Previously when assessing a dataset before running a linear regression, the possibility of outliers would be assessed using histograms and scatterplots. Both methods of assessing data points were subjective and there was little way of knowing how much leverage each potential outlier had on the results data.
Multiverse analysis is a scientific method that specifies and then runs a set of plausible alternative models or statistical tests for a single hypothesis. [1] It is a method to address the issue that the "scientific process confronts researchers with a multiplicity of seemingly minor, yet nontrivial, decision points, each of which may introduce variability in research outcomes". [2]
Galton's experimental setup "Standard eugenics scheme of descent" – early application of Galton's insight [1]. In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where if one sample of a random variable is extreme, the next sampling of the same random variable is likely to be closer to its mean.
Peirce's criterion does not depend on observation data (only characteristics of the observation data), therefore making it a highly repeatable process that can be calculated independently of other processes. This feature makes Peirce's criterion for identifying outliers ideal in computer applications because it can be written as a call function.
First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme 'impact'. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.