Jove
Visualize
Contact Us

Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.4K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.4K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

344
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
344
Hindsight Biases01:12

Hindsight Biases

3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
3.4K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.8K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.8K

You might also read

Related Articles

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

Sort by
Same author

Safety versus performance: How multi-objective learning reduces barriers to market entry.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Deep generative modeling of sample-level heterogeneity in single-cell genomics.

Nature methods·2025
Same author

Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction.

Nature methods·2025
Same author

Functional protein mining with conformal guarantees.

Nature communications·2025
Same author

Cross-prediction-powered inference.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Generative models for protein structures and sequences.

Nature biotechnology·2024
Same journal

A native sulfur deposit in Gale crater, Mars.

Science (New York, N.Y.)·2026
Same journal

Coordinated demise of harmful algal blooms.

Science (New York, N.Y.)·2026
Same journal

Genetic effects put into context.

Science (New York, N.Y.)·2026
Same journal

Bacteria share proteins to survive antibiotics.

Science (New York, N.Y.)·2026
Same journal

Impacts shaped Earth's first continents.

Science (New York, N.Y.)·2026
Same journal

Erratum for the Report "Covalently bonded single-molecule junctions with stable and reversible photoswitched conductivity" by C. Jia <i>et al</i>.

Science (New York, N.Y.)·2026
See all related articles
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 Experiment Video

Updated: Jul 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K

Prediction-powered inference.

Anastasios N Angelopoulos1, Stephen Bates1, Clara Fannjiang1

  • 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA.

Science (New York, N.Y.)
|November 9, 2023
PubMed
Summary
This summary is machine-generated.

Prediction-powered inference offers valid statistical inference by combining experimental data with machine learning predictions. This approach provides accurate confidence intervals, enabling more data-efficient research across various scientific fields.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.6K

Related Experiment Videos

Last Updated: Jul 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

2.6K

Area of Science:

  • Statistical inference
  • Machine learning applications
  • Data science

Background:

  • Traditional statistical inference often requires strict assumptions.
  • Machine learning models can provide powerful predictive capabilities.
  • Integrating predictions into inference can enhance statistical validity and efficiency.

Purpose of the Study:

  • To introduce prediction-powered inference, a novel framework for statistical analysis.
  • To demonstrate the ability to compute provably valid confidence intervals.
  • To show that improved machine learning predictions lead to narrower confidence intervals.

Main Methods:

  • Developing algorithms for valid statistical inference using machine learning predictions.
  • Applying the framework without assumptions on the underlying machine learning model.
  • Testing the methodology across diverse datasets.

Main Results:

  • The framework provides simple algorithms for valid confidence intervals for means, quantiles, and regression coefficients.
  • Accuracy of machine learning predictions directly impacts confidence interval width.
  • Successful application demonstrated across proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.

Conclusions:

  • Prediction-powered inference enables valid and more data-efficient conclusions in research.
  • The framework is versatile and applicable across multiple scientific domains.
  • It offers a robust method for leveraging machine learning in statistical analysis.