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Related Concept Videos

Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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Modified Boxplots00:57

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Related Experiment Video

Updated: Apr 14, 2026

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
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Outlier detection in psychiatric epidemiology

M Leese

    Epidemiologia E Psichiatria Sociale
    |February 5, 1998
    PubMed
    Summary

    Outlier detection is increasingly important in medicine and psychiatry, moving beyond simple statistics to address complex data. Advanced statistical methods are crucial for identifying unusual cases and improving data interpretation.

    Area of Science:

    • Statistics
    • Medical Research
    • Psychiatry

    Background:

    • Traditionally, medical and psychiatric studies focused on summary statistics and simple models.
    • Outlier detection is gaining prominence due to medical audit and political factors.
    • Identifying outliers is becoming a subject of public interest in healthcare.

    Discussion:

    • Statistical methods can clarify the uncertainty in rankings, contextualizing "best" and "worst" labels for institutions and professionals.
    • Bayesian methods are pivotal for addressing uncertainty in outlier analysis.
    • Computer-intensive techniques are advancing the detection of multiple and multivariate outliers.

    Key Insights:

    • Outliers can be significant in themselves or act as distractors, impacting generalization.

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  • The focus is shifting from solely generalizable findings to the specific identification of outliers.
  • Statistical rigor is essential for accurate outlier identification and interpretation.

    • Outlook:

    • Developments in outlier detection are crucial for complex datasets, particularly in psychiatric epidemiology.
    • Tailored outlier detection methods are emerging for specific models like factor analysis and meta-analysis.
    • Future research will likely involve sophisticated computational approaches for nuanced outlier analysis.