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

Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis01:24

Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis

874
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...
874
Skewness01:06

Skewness

21.6K
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...
21.6K
Modified Boxplots00:57

Modified Boxplots

11.8K
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...
11.8K
Types of Skewness01:09

Types of Skewness

21.0K
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,...
21.0K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

8.4K
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...
8.4K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

5.0K
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...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action.

The American statistician·2019
Same author

Teaching Principal Components Using Correlations.

Multivariate behavioral research·2017
Same author

Closed Testing in Pharmaceutical Research: Historical and Recent Developments.

Statistics in biopharmaceutical research·2015
Same author

Multiplicity and replicability: two sides of the same coin.

Pharmaceutical statistics·2014
Same author

Analysis and correction of crosstalk effects in pathway analysis.

Genome research·2013
Same author

On using the bootstrap for multiple comparisons.

Journal of biopharmaceutical statistics·2011

Related Experiment Video

Updated: Apr 17, 2026

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

10.3K

Kurtosis as Peakedness, 1905 - 2014. R.I.P.

Peter H Westfall1

  • 1Peter H. Westfall is Horn Professor in the Area of Information Systems and Quantitative Sciences, Texas Tech University, Lubbock, TX 79409 ( peter.westfall@ttu.edu ).

The American Statistician
|February 14, 2015
PubMed
Summary

Kurtosis does not measure distribution peakedness. This statistical measure unambiguously indicates tail extremity and the propensity for outliers, not central shape.

Keywords:
Fourth MomentInequalityLeptokurticMesokurticPlatykurtic

More Related Videos

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.6K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Related Experiment Videos

Last Updated: Apr 17, 2026

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

10.3K
Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.6K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Area of Science:

  • Statistics
  • Data Analysis
  • Probability Theory

Background:

  • A persistent misconception equates kurtosis with the peakedness or modality of a distribution.
  • Statistical literature has attempted to correct this misunderstanding regarding kurtosis interpretation.

Purpose of the Study:

  • To definitively refute the notion that kurtosis measures distribution peakedness.
  • To clarify the unambiguous interpretation of kurtosis as a measure of tail extremity.

Main Methods:

  • Review of relevant statistical literature on kurtosis.
  • Presentation of counterexample distributions to illustrate kurtosis properties.
  • Analysis of the proportion of kurtosis determined by the central μ ± σ range.

Main Results:

  • Kurtosis provides virtually no information about the shape of a distribution's peak.
  • The only unambiguous interpretation of kurtosis relates to tail extremity and outlier presence or propensity.
  • The contribution of the central μ ± σ range to the overall kurtosis is typically minimal.

Conclusions:

  • The interpretation of kurtosis as a measure of peakedness is incorrect and should be disregarded.
  • Kurtosis is solely an indicator of tail behavior and outlier characteristics in statistical distributions.