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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

491
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
491
Statistical Significance01:50

Statistical Significance

22.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.2K
Confidence Coefficient01:24

Confidence Coefficient

10.7K
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...
10.7K
Confidence Intervals01:21

Confidence Intervals

10.8K
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...
10.8K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.1K
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...
10.1K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.7K
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...
11.7K

You might also read

Related Articles

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

Sort by
Same author

Editorial: Harnessing invasive plant species into valuable products.

Frontiers in plant science·2026
Same author

TDGF1 Mediates the Oncogenic Effects of the OLMALINC/miR-3614-5p ceRNA Axis in Colon Cancer Through Nodal/Smad2 and Glypican-1/MAPK-AKT Signaling.

Cells·2026
Same author

Early versus delayed urethroplasty for pediatric pelvic fracture-related urethral injury in boys: a retrospective comparative cohort study of complications and surgical success rates from a single center.

Translational andrology and urology·2026
Same author

Integrating ANP32A expression with Ann Arbor stage refines prognostic stratification in extranodal NK/T-cell lymphoma.

Diagnostic pathology·2026
Same author

Optimized protocols for culturing and sectioning mouse intestinal organoids: enhancing efficiency and structural integrity.

MethodsX·2026
Same author

Integrated genomic and immunophenotypic profiling reveals monoclonal origin, smoking-driven evolution and heterogeneous microenvironment in pulmonary adenosquamous carcinoma.

Frontiers in immunology·2026

Related Experiment Video

Updated: Feb 10, 2026

Generation of Alginate Microspheres for Biomedical Applications
10:33

Generation of Alginate Microspheres for Biomedical Applications

Published on: August 12, 2012

21.9K

Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference.

Zhiling Gu1, Shan Yu2, Guannan Wang3

  • 1Department of Biostatistics, Yale University, New Haven, CT, 06510.

Journal of the American Statistical Association
|February 9, 2026
PubMed
Summary

Generative artificial intelligence (AI) creates synthetic biomedical images. This study introduces a new method to compare original and synthetic data, ensuring AI-generated images are reliable for research.

Keywords:
Biomedical imaging synthesisFunctional principal component analysisSimultaneous confidence regionsSurface-based imaging dataTriangulated spherical splines

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.5K
Fluorescence Lifetime Macro Imager for Biomedical Applications
06:01

Fluorescence Lifetime Macro Imager for Biomedical Applications

Published on: April 7, 2023

1.2K

Related Experiment Videos

Last Updated: Feb 10, 2026

Generation of Alginate Microspheres for Biomedical Applications
10:33

Generation of Alginate Microspheres for Biomedical Applications

Published on: August 12, 2012

21.9K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.5K
Fluorescence Lifetime Macro Imager for Biomedical Applications
06:01

Fluorescence Lifetime Macro Imager for Biomedical Applications

Published on: April 7, 2023

1.2K

Area of Science:

  • Biomedical Imaging
  • Artificial Intelligence
  • Data Science

Background:

  • Generative AI advances biomedical imaging by synthesizing data, but ensuring data fidelity and utility remains crucial.
  • Challenges in data availability, privacy, and diversity necessitate reliable synthetic biomedical imaging data.
  • Evaluating the statistical differences between original and synthetic imaging data is essential for trustworthy AI applications.

Purpose of the Study:

  • To develop a novel nonparametric method for comparing mean and covariance functions of original and synthetic biomedical imaging data.
  • To quantify uncertainty in differences between original and synthetic data using simultaneous confidence regions (SCRs).
  • To enhance the fidelity and utility of synthetic biomedical imaging data for research.

Main Methods:

  • Functional data analysis framework with triangulated spherical splines for surface-based imaging data.
  • Construction and asymptotic property establishment of simultaneous confidence regions (SCRs).
  • Application to Human Connectome Project brain imaging data to compare original and synthetic images.

Main Results:

  • The proposed SCRs provide exact coverage probabilities and demonstrate equivalence to noise-free data.
  • Simulation studies validated SCR coverage properties and hypothesis test performance.
  • Significant differences were identified between original and synthetic brain imaging data from the Human Connectome Project.

Conclusions:

  • A novel method effectively identifies differences between original and synthetic biomedical imaging data.
  • A transformation technique can align synthetic data's statistical properties with original data.
  • Improved synthetic data reliability enhances its utility for biomedical research.