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  2. Outlier - Wikipedia

    en.wikipedia.org/wiki/Outlier

    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.

  3. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    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.

  4. Chauvenet's criterion - Wikipedia

    en.wikipedia.org/wiki/Chauvenet's_criterion

    The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...

  5. Peirce's criterion - Wikipedia

    en.wikipedia.org/wiki/Peirce's_criterion

    The outliers would greatly change the estimate of location if the arithmetic average were to be used as a summary statistic of location. The problem is that the arithmetic mean is very sensitive to the inclusion of any outliers; in statistical terminology, the arithmetic mean is not robust .

  6. Random sample consensus - Wikipedia

    en.wikipedia.org/wiki/Random_sample_consensus

    A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.

  7. Leverage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Leverage_(statistics)

    The formula then divides by () to account for the fact that we remove the observation rather than adjusting its value, reflecting the fact that removal changes the distribution of covariates more when applied to high-leverage observations (i.e. with outlier covariate values). Similar formulas arise when applying general formulas for statistical ...

  8. Standard deviation - Wikipedia

    en.wikipedia.org/wiki/Standard_deviation

    The standard deviation is commonly used in the determination of what constitutes an outlier and what does not. Standard deviation may be abbreviated SD or std dev , and is most commonly represented in mathematical texts and equations by the lowercase Greek letter σ (sigma), for the population standard deviation, or the Latin letter s , for the ...

  9. Noisy data - Wikipedia

    en.wikipedia.org/wiki/Noisy_data

    If actual outliers are not removed from the data set, they corrupt the results to a small or large degree depending on circumstances. If valid data is identified as an outlier and is mistakenly removed, that also corrupts results. Fraud: Individuals may deliberately skew data to influence the results toward a desired conclusion.