Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Skewness01:09

Types of Skewness

If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
Skewness01:06

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...
Introduction to Normal Distributions01:29

Introduction to Normal Distributions

Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis01:24

Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis

Central tendency refers to the central point or typical value of a dataset. It summarizes the data set with a single value that represents the center of its distribution. The three main measures of central tendency are:
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
Data: Types and Distribution01:19

Data: Types and Distribution

In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Normal Distribution01:11

Normal Distribution

The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is extremely...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Syntheses and catalytic oxotransfer activities of oxo molybdenum(vi) complexes of a new aminoalcohol phenolate ligand.

Dalton transactions (Cambridge, England : 2003)·2017
Same author

Aminobisphenolate supported tungsten disulphido and dithiolene complexes.

Dalton transactions (Cambridge, England : 2003)·2015
Same author

Vanadium complexes with multidentate amine bisphenols.

Dalton transactions (Cambridge, England : 2003)·2014
Same author

Modelling the burden of stroke in Finland until 2030.

International journal of stroke : official journal of the International Stroke Society·2009
Same author

Prognosis of depression with and without dementia in old age.

Archives of gerontology and geriatrics·2008
Same author

Long-term prognosis after coronary artery bypass surgery.

International journal of cardiology·2007
Same journal

Precision medicine in mental health: applications, challenges, and recommendations - CORRIGENDUM.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same journal

Real-world effectiveness and safety of intranasal esketamine for treatment-resistant depression: data from the enTRD registry.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same journal

Trajectory of response to esketamine nasal spray for treatment resistant depression: findings from ESCAPE-TRD.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same journal

Identification of distinct clinical phenotypes and their neurobiological signatures in stress-exposed individuals: A multimodal machine learning approach.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same journal

The interplay among narcissistic vulnerability, interpersonal sensitivity, and metacognitive integration: A network analysis approach.

European psychiatry : the journal of the Association of European Psychiatrists·2026
Same journal

A Qualitative Exploration of Women's Experiences, Stigma, and Access to Care in Gaming Disorder.

European psychiatry : the journal of the Association of European Psychiatrists·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice
06:15

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice

Published on: July 15, 2025

Modelling psychiatric measures using Skew-Normal distributions.

N Counsell1, M Cortina-Borja, A Lehtonen

  • 1Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK. Nicholas.Counsell@psych.ox.ac.uk

European Psychiatry : the Journal of the Association of European Psychiatrists
|November 2, 2010
PubMed
Summary
This summary is machine-generated.

Psychiatric research data often deviate from normal distributions. We address modeling skewness in data, common in screening healthy populations, to better represent disorder prevalence.

More Related Videos

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
13:08

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

Related Experiment Videos

Last Updated: Jun 7, 2026

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice
06:15

A Multiple Integrated Social Stress Model for Psychiatric Disorders in Female C57BL/6J Mice

Published on: July 15, 2025

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
13:08

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

Area of Science:

  • Psychiatry
  • Statistical Modeling
  • Data Analysis

Background:

  • Psychiatric research data frequently display non-normal distributions.
  • Standard statistical methods may be suboptimal when data deviates significantly from normality.
  • Screening instruments often yield skewed data due to a majority of healthy respondents.

Purpose of the Study:

  • To highlight the challenges of modeling skewed data in psychiatric research.
  • To emphasize the need for methods that optimally utilize non-normal data.
  • To address the specific issue of skewness arising from screening instruments.

Main Methods:

  • Utilizing statistical methods designed for direct distribution modeling.
  • Focusing on techniques that can optimally handle non-normal data.
  • Analyzing data from screening instruments with a high proportion of healthy individuals.

Main Results:

  • Departures from normality are common in psychiatric datasets.
  • Skewness is a prevalent issue, particularly in data from screening tools.
  • Available methods can effectively model non-normal distributions directly.

Conclusions:

  • Directly modeling data distributions is crucial for accurate psychiatric research.
  • Special attention must be paid to skewness when analyzing screening instrument data.
  • Optimal data utilization requires methods that accommodate non-normality.