Jove
Visualize
Contact Us

Related Concept Videos

Data Validation01:15

Data Validation

317
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
317
Data Validation01:03

Data Validation

5.9K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.9K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

10.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
10.6K
Statistical Significance01:50

Statistical Significance

20.6K
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...
20.6K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

3.8K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
3.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.8K
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...
2.8K

You might also read

Related Articles

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

Sort by
Same author

A Polymer Blend Approach for Creation of Effective Conjugated Polymer Charge Transport Pathways.

ACS applied materials & interfaces·2018
Same author

Microfluidic Crystal Engineering of π-Conjugated Polymers.

ACS nano·2015
Same author

Photoinduced Anisotropic Assembly of Conjugated Polymers in Insulating Polymer Blends.

ACS applied materials & interfaces·2015
Same journal

The influence of chirality on the macroscopic behavior of multiferroic smectic phases.

The Journal of chemical physics·2026
Same journal

Polaron transformed canonically consistent quantum master equation.

The Journal of chemical physics·2026
Same journal

The x-ray absorption spectrum of the propargyl radical C3H3●.

The Journal of chemical physics·2026
Same journal

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. I. Conformer- and isomer-resolved infrared spectra.

The Journal of chemical physics·2026
Same journal

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. II. Isomer-resolved unimolecular dynamics.

The Journal of chemical physics·2026
Same journal

Quantum state-to-state dynamics studies of the C(3P) + OH(X2Π) → CO(a3Π) + H(2S) reaction based on a new HCO(12A″) potential energy surface.

The Journal of chemical physics·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: Oct 25, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Aggressively optimizing validation statistics can degrade interpretability of data-driven materials models.

Katherine Lei1, Howie Joress2, Nils Persson2

  • 1Montgomery Blair High School, 57 University Blvd E., Silver Spring, Maryland 20901, USA.

The Journal of Chemical Physics
|August 8, 2021
PubMed
Summary
This summary is machine-generated.

Ignoring less impactful hyperparameters in artificial intelligence models can reduce explainability in materials science. Optimizing models using a Pareto strategy balances predictive power and interpretability, ensuring reliable AI-driven insights.

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.7K
Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

43.0K

Related Experiment Videos

Last Updated: Oct 25, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.7K
Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

43.0K

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Trust in artificial intelligence (AI) within materials science relies heavily on model interpretability.
  • Understanding feature importance allows scientists to validate AI predictions and discover new material properties.

Purpose of the Study:

  • To investigate how hyperparameter choices affect the explainability of machine learning models in materials science.
  • To demonstrate that commonly accepted practices may inadvertently reduce model interpretability.

Main Methods:

  • Trained random forest models for high entropy alloy classification.
  • Evaluated model explainability using impurity, permutation, and Shapley importance rankings.
  • Compared standard hyperparameter optimization (validation accuracy) with a Pareto optimization strategy balancing training and validation statistics.

Main Results:

  • Models trained with unconstrained maximum depths often identify random features as important predictors.
  • Standard optimization focusing solely on validation accuracy can lead to models prioritizing random features.
  • Pareto optimization successfully yielded models that rejected random features, balancing predictive power and explainability.

Conclusions:

  • Hyperparameter selection critically impacts AI model explainability in materials science.
  • A Pareto optimization approach offers a robust method to enhance model interpretability without sacrificing predictive accuracy.
  • This strategy is crucial for building trust and enabling deeper insights from AI in materials discovery.