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

Normal Distribution01:11

Normal Distribution

18.3K
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...
18.3K
Introduction to Normal Distributions01:29

Introduction to Normal Distributions

186
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...
186
Data: Types and Distribution01:19

Data: Types and Distribution

2.2K
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...
2.2K
Probability Distributions01:32

Probability Distributions

13.3K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
13.3K
Applications of Normal Distribution01:22

Applications of Normal Distribution

10.1K
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
10.1K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

30.4K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
30.4K

You might also read

Related Articles

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

Sort by
Same author

Incidence of autoimmune diseases in children with atopy treated with dupilumab.

The Journal of allergy and clinical immunology·2026
Same author

Leveraging the PEDSnet clinical research network and electronic health record data to enhance efficiency of trial enrollment for a rare pediatric rheumatic disease.

Pediatric rheumatology online journal·2026
Same author

Multi-site analysis of COVID-19 and new-onset diabetes reveals need for improved sensitivity of EHR-based COVID-19 phenotypes-a DiCAYA Network analysis.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Using a multi-institutional pediatric learning health system to characterize induction therapy for incident lupus nephritis in children.

Pediatric nephrology (Berlin, Germany)·2025
Same author

Respiratory and Other Infections Following COVID.

Pediatrics·2025
Same author

Hypertension, proteinuria, and RAAS inhibition use in children with systemic lupus erythematosus: Data from a multi-institutional pediatric learning health system.

Lupus·2025

Related Experiment Video

Updated: Mar 28, 2026

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

Understanding a Normal Distribution of Data (Part 2).

Mitchell Maltenfort1

  • 1Rothman Institute, Philadelphia, PA.

Clinical Spine Surgery
|December 24, 2015
PubMed
Summary

Analyzing non-normal data requires advanced methods like data transformation, Generalized Linear Modeling, and bootstrapping. Understanding their strengths and weaknesses is key, often needing statistical expertise for accurate analysis.

Area of Science:

  • Statistics
  • Data Analysis

Background:

  • Data normality is a common assumption in statistical analysis.
  • Violations of normality can impact the validity of results.
  • Advanced methods are needed for non-normal data.

Purpose of the Study:

  • To discuss advanced techniques for analyzing non-normal data.
  • To compare the strengths and weaknesses of these methods.
  • To guide researchers in selecting appropriate strategies.

Main Methods:

  • Data transformation techniques.
  • Generalized Linear Modeling (GLM).
  • Bootstrapping methods.

Main Results:

  • Each technique offers different approaches to handle non-normal data.

More Related Videos

Extracting the Young's Modulus of Native Murine Pulmonary Basement Membranes from Atomic Force Microscopy Derived Force Maps
10:55

Extracting the Young's Modulus of Native Murine Pulmonary Basement Membranes from Atomic Force Microscopy Derived Force Maps

Published on: January 31, 2025

1.1K
Analysis and Specification of Starch Granule Size Distributions
08:46

Analysis and Specification of Starch Granule Size Distributions

Published on: March 4, 2021

5.7K

Related Experiment Videos

Last Updated: Mar 28, 2026

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
Extracting the Young's Modulus of Native Murine Pulmonary Basement Membranes from Atomic Force Microscopy Derived Force Maps
10:55

Extracting the Young's Modulus of Native Murine Pulmonary Basement Membranes from Atomic Force Microscopy Derived Force Maps

Published on: January 31, 2025

1.1K
Analysis and Specification of Starch Granule Size Distributions
08:46

Analysis and Specification of Starch Granule Size Distributions

Published on: March 4, 2021

5.7K
  • Data transformation can simplify data but may distort relationships.
  • Generalized Linear Modeling and bootstrapping provide robust alternatives.

    • Conclusions:

    • Choosing the right method depends on the specific data and research question.
    • Expert statistical consultation is often recommended for complex non-normal data analysis.