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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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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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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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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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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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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.
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Cutaneous Bioavailability of Corticosteroids from Topical Formulations: a Retrospective Analysis of Data from In Vitro Permeation Testing (IVPT) and In Vivo Assessments.

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Observations on the Tritiated Water and TEWL Skin Integrity Tests: Relevance to In Vitro Permeation Testing (IVPT).

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A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
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IVPT Data Challenges in the Real World: Outliers, Anomalous and Aberrant Data: Examples.

Paul A Lehman1

  • 1Independent Scientist, Sumner, WA, 98390, USA. palehman@yahoo.com.

Pharmaceutical Research
|July 16, 2025
PubMed
Summary

Outliers in in vitro permeation tests (IVPT) often indicate genuine skin anomalies, not method flaws. Proper analysis of these outliers and missing data is crucial for accurate topical formulation bioequivalence (BE) determination.

Keywords:
IVPTpercutaneous absorptionskin permeabilitytopical bioequivalence

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Area of Science:

  • Dermatology
  • Pharmacokinetics
  • Toxicology

Background:

  • The in vitro permeation test (IVPT) is a key method for studying skin absorption of chemicals and drugs.
  • IVPT allows multiple skin replicates from a single donor, offering detailed kinetic analysis.
  • Anomalous absorption profiles ('outliers') can occur within replicates from the same donor.

Purpose of the Study:

  • To investigate the nature and implications of data outliers in IVPT.
  • To address issues of anomalous data, missing data, and representative Jmax determination in IVPT.
  • To differentiate true biological variability from methodological artifacts in skin permeation studies.

Main Methods:

  • Retrospective analysis of data from the authors' archives.
  • Screening for and evaluating skin section and donor data outliers.
  • Assessing anomalous and missing data points.
  • Investigating methods for establishing a representative maximum flux (Jmax).

Main Results:

  • Outliers in IVPT typically reflect genuine anomalies in donor skin or population, not the test method itself.
  • Statistical approaches are effective in distinguishing anomalous data from the main dataset.
  • Missing data and challenges in determining a representative Jmax can hinder accurate bioequivalence (BE) assessment.

Conclusions:

  • When properly conducted, IVPT outliers are informative about biological variability.
  • Identifying and statistically validating outliers is essential for robust IVPT data interpretation.
  • Accurate determination of bioequivalence for topical products relies on addressing data anomalies and missing values.