Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Standard Error of the Mean

Standard Error of the Mean

The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...

Empirical Method to Interpret Standard Deviation

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...

Calculating Standard Deviation

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high variation.
Let us...

Estimating Population Standard Deviation

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

Standard Deviation

Standard Deviation

The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more...

Chebyshev's Theorem to Interpret Standard Deviation

Chebyshev's Theorem to Interpret Standard Deviation

Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:

You might also read

Related Articles

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

Sort by

Same author

Primer on reporting statistics: kayaks and walking trees once more.

Advances in physiology education·2023

Same author

Factors Associated with Persistence of Severe Asthma from Late Adolescence to Early Adulthood.

American journal of respiratory and critical care medicine·2021

Same author

Anatomic evaluation of radiographic landmarks for accurate straight antegrade intramedullary nail placement in the humerus.

JSES international·2020

Same author

MnTE-2-PyP disrupts Staphylococcus aureus biofilms in a novel fracture model.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society·2020

Same author

Stability of Do-Not-Resuscitate Orders in Hospitalized Adults: A Population-Based Cohort Study.

Critical care medicine·2020

Same author

The atopic march and Staphylococcus aureus colonization are associated with fall birth.

The journal of allergy and clinical immunology. In practice·2020

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

Explorations in statistics: standard deviations and standard errors.

Douglas Curran-Everett¹

¹Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO 80206, USA. EverettD@njc.org

Advances in Physiology Education

|September 17, 2008

Summary

This summary is machine-generated.

Understanding statistics involves active exploration, similar to science. This series uses R software to clarify the difference between standard deviation and standard error, crucial for accurate data reporting.

More Related Videos

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Related Experiment Videos

Last Updated: Jul 1, 2026

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Area of Science:

Physiology Education
Biostatistics

Background:

Statistical concepts are fundamental to scientific research.
Active learning enhances understanding of complex statistical principles.

Purpose of the Study:

To differentiate between standard deviation and standard error.
To illustrate the application of these concepts using R software.

Main Methods:

Utilizing the R software package for statistical exploration.
Investigating the fundamental distinction between standard deviation and standard error.

Main Results:

Standard deviation quantifies variability within sample observations.
Standard error of the mean estimates the variability of theoretical sample means, reflecting uncertainty in the population mean.

Conclusions:

Reporting standard deviation is essential for comprehensive data reporting.
Standard error of the mean, incorporating sample size, estimates population mean uncertainty rather than observation variability.