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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.
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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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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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Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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Identifying outliers and implausible values in growth trajectory data.

Seungmi Yang1, Jennifer A Hutcheon2

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal Canada.

Annals of Epidemiology
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Conditional growth percentiles effectively identify implausible weight measurements in growth trajectory data. This systematic approach is crucial for large datasets, improving data accuracy in pediatric research.

Keywords:
Data cleaningLongitudinal growth dataOutliers identification

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Area of Science:

  • Pediatric growth monitoring
  • Biostatistics
  • Data quality assessment

Background:

  • Accurate growth trajectory data is essential for child health monitoring.
  • Traditional methods for identifying implausible measurements may miss outliers in longitudinal data.
  • Large datasets present challenges for manual outlier detection.

Purpose of the Study:

  • To demonstrate the utility of conditional growth percentiles for systematically identifying implausible measurements in growth trajectory data.
  • To adapt conditional growth percentiles for outlier detection in serial weight measurements.
  • To provide a reproducible method for enhancing data quality in growth studies.

Main Methods:

  • Applied conditional growth percentiles to serial weight measurements (N=86,427) from birth to 6.5 years in 8217 girls.
  • Calculated conditional mean and variance for each measurement based on prior weights.
  • Flagged measurements exceeding 4 standard deviations (SD) from the conditional expected weight as outliers.

Main Results:

  • Identified 234 weight measurements (0.3%) from 216 girls as potential outliers.
  • Confirmed the implausibility of flagged measurements upon trajectory review.
  • Demonstrated superior detection of outliers compared to conventional cross-sectional methods.

Conclusions:

  • Conditional growth percentiles offer a systematic method for identifying implausible values in growth trajectory data.
  • This approach is particularly valuable for large datasets where manual review is infeasible.
  • The method enhances the reliability of growth data analysis in pediatric research.