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Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

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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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What Are Outliers?01:12

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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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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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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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Detection of Gross Error: The Q Test01:00

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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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Significance Testing: Overview01:04

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Modified Boxplots00:57

Modified Boxplots

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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.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
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Outlier and influence diagnostics for meta-analysis.

Wolfgang Viechtbauer1, Mike W-L Cheung2

  • 1Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands. wolfgang.viechtbauer@stat.unimaas.nl.

Research Synthesis Methods
|June 11, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces methods to identify outliers and influential cases in meta-analyses, ensuring more reliable research conclusions. These diagnostics are crucial for the validity and robustness of meta-analytic findings.

Keywords:
influence diagnosticsmeta‐analysismixed‐effects modeloutliers

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • Outliers and influential cases can compromise meta-analysis validity.
  • Standard diagnostic procedures for meta-analysis are limited.
  • Examining influential cases is essential for robust meta-analytic conclusions.

Purpose of the Study:

  • To extend standard diagnostic procedures from linear regression to meta-analysis.
  • To provide methods for identifying outliers and influential cases in meta-analysis.
  • To discuss practical issues concerning these diagnostic procedures.

Main Methods:

  • Extension of standard diagnostic procedures from linear regression.
  • Application to meta-analytic fixed-effects models.
  • Application to meta-analytic random/mixed-effects models.

Main Results:

  • Demonstrated the utility of extended diagnostic procedures through three examples.
  • Provided a framework for assessing outlier and influential case diagnostics in meta-analysis.
  • Highlighted practical considerations for implementing these methods.

Conclusions:

  • The proposed diagnostic procedures enhance the validity and robustness of meta-analytic findings.
  • These methods are applicable across various research settings.
  • Further discussion on diagnostic procedures in meta-analysis is warranted.