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

Confidence Intervals01:21

Confidence Intervals

9.3K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
9.3K
Bootstrapping01:24

Bootstrapping

789
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
789
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.8K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
8.8K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

9.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
9.9K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.6K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.6K
Confidence Coefficient01:24

Confidence Coefficient

9.2K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
9.2K

You might also read

Related Articles

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

Sort by
Same author

The cost of coping with infertility: Extending theory on stressor appraisal.

The Journal of applied psychology·2026
Same author

Optimizing facility-specific urinary weighted-incidence syndromic antibiograms for nursing homes.

Infection control and hospital epidemiology·2026
Same author

Bias and discrimination perceived by antimicrobial stewards: a mixed-methods study.

Infection control and hospital epidemiology·2025
Same author

Thinning restores ungulate foraging habitat in historically logged forests.

Ecological applications : a publication of the Ecological Society of America·2025
Same author

Mediation testing with polynomial regression: A critical review of extant approaches and a researcher's toolkit for the future.

The Journal of applied psychology·2025
Same author

AncestryGeni: a novel genetic ancestry classification pipeline for small and noisy sequence data.

Bioinformatics (Oxford, England)·2025
Same journal

Grateful leaders and attentive followers: How grateful attention transmits the cascade of gratitude expressions to strengthen coworker relationships.

The Journal of applied psychology·2026
Same journal

Scoring employment interviews with large language models: Evaluation design components, validity investigations, and best practice recommendations.

The Journal of applied psychology·2026
Same journal

A profile analysis of leader interpersonal emotion management strategies.

The Journal of applied psychology·2026
Same journal

A choice architecture intervention to increase diversity: Diverse defaults can counteract hiring discrimination.

The Journal of applied psychology·2026
Same journal

Transformational leadership in context: A meta-analysis of 40 years of research.

The Journal of applied psychology·2026
Same journal

Organizations espousing an authenticity ideology repel stigmatized job seekers.

The Journal of applied psychology·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.1K

Small sample mediation testing: misplaced confidence in bootstrapped confidence intervals.

Joel Koopman1, Michael Howe1, John R Hollenbeck1

  • 1Department of Management.

The Journal of Applied Psychology
|April 16, 2014
PubMed
Summary
This summary is machine-generated.

Bootstrapping for mediation analysis in small samples (20-80 cases) often lacks statistical power and inflates Type I errors. Alternative resampling and Bayesian methods offer better performance without these issues.

More Related Videos

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

11.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.0K

Related Experiment Videos

Last Updated: May 1, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.1K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

11.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.0K

Area of Science:

  • Psychology
  • Statistics
  • Quantitative Research Methods

Background:

  • Bootstrapping is widely used in psychology to test mediation models, especially with small sample sizes (20-80 cases).
  • Its application has increased in journals like the Journal of Applied Psychology for small sample mediation analysis.

Purpose of the Study:

  • To evaluate the statistical rigor of bootstrapping for mediation in small samples.
  • To investigate alternative methods for hypothesis testing in small sample mediation.

Main Methods:

  • A simulation study focused on sample sizes ranging from 20 to 80 cases.
  • Comparison of bootstrapping with an alternative empirical resampling method and a Bayesian approach.

Main Results:

  • Bootstrapping demonstrated insufficient statistical power and an inflated Type I error rate in most conditions for small samples.
  • Alternative resampling and Bayesian methods showed comparable statistical power to bootstrapping without inflated Type I error rates.

Conclusions:

  • Researchers should exercise caution when using bootstrapping for mediation analysis with small samples.
  • Alternative resampling and Bayesian approaches are recommended for more rigorous hypothesis testing in small samples.