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

What Are Outliers?

What Are Outliers?

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.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...

Outliers and Influential Points

Outliers and Influential Points

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...

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

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...

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

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...

Unusual Results

Unusual Results

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 =...

Modified Boxplots

Modified Boxplots

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...

You might also read

Related Articles

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

Sort by

Same author

Sub-lethal effects of natural cyanobacterial blooms on fish: Enzymatic activity and swimming performance in Gasterosteus aculeatus.

Harmful algae·2025

Same author

Questionable Research Practices: HARKing.

The Journal of nursing education·2025

Same author

Questionable Research Practices: Cherry Picking.

The Journal of nursing education·2025

Same author

Questionable Research Practices: <i>p</i>-Hacking.

The Journal of nursing education·2025

Same author

Lung Cancer and Palliative Care Support: The Role of Social Determinants of Health for Older Adult Patients.

Oncology nursing forum·2025

Same author

Adopting Optimal Statistical Practices: Reviewers.

The Journal of nursing education·2024

Same journal

AI Utilization and Clinical Judgment: Predictors of Caring Behavior Among Nursing Students.

The Journal of nursing education·2026

Same journal

Mental Health Nursing Simulation to Develop the Therapeutic Use of Self.

The Journal of nursing education·2026

Same journal

Cultivating Clinical Judgment Through Wound Building: A Teaching Innovation.

The Journal of nursing education·2026

Same journal

Pathophysiology as a Predictor of Success in a Prelicensure Undergraduate Nursing Program.

The Journal of nursing education·2026

Same journal

Perceived Faculty Support and Nursing Student Stress and Anxiety: A Systematic Review.

The Journal of nursing education·2026

Same journal

Nursing Students' Ethical Perspectives on Artificial Intelligence: Implications for Nursing Education.

The Journal of nursing education·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

Conditions Affecting Social Space in Drosophila melanogaster

Conditions Affecting Social Space in Drosophila melanogaster

Published on: November 5, 2015

Check for Outliers.

The Journal of Nursing Education

|March 6, 2026

Summary

This summary is machine-generated.

Researchers must manage outliers in nursing education studies to prevent errors. This column emphasizes checking for and reporting outlier methodologies and results for valid scientific research.

More Related Videos

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Author Spotlight: Advancing Organoid Generation for Drug Development Using iPSCs

Author Spotlight: Advancing Organoid Generation for Drug Development Using iPSCs

Published on: March 15, 2024

Related Experiment Videos

Last Updated: Mar 7, 2026

Conditions Affecting Social Space in Drosophila melanogaster

Conditions Affecting Social Space in Drosophila melanogaster

Published on: November 5, 2015

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Author Spotlight: Advancing Organoid Generation for Drug Development Using iPSCs

Author Spotlight: Advancing Organoid Generation for Drug Development Using iPSCs

Published on: March 15, 2024

Area of Science:

Nursing Education Research
Scientific Methodology

Background:

Outliers can introduce significant errors into research studies.
Improper management of outliers compromises the validity of scientific findings.

Purpose of the Study:

To highlight the importance of outlier management in nursing education research.
To encourage applied researchers to rigorously address outliers in their studies.

Main Methods:

This column entry focuses on the conceptual approach to outlier management.
It emphasizes the critical review of existing research practices.

Main Results:

Studies ignoring or mismanaging outliers risk introducing errors.
Clear reporting of outlier methodology and results is crucial.

Conclusions:

Applied researchers in nursing education must proactively identify and manage outliers.
Transparent reporting of outlier handling enhances research integrity and validity.