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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Prediction Intervals01:03

Prediction Intervals

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. 
The...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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,...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

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Related Experiment Video

Updated: Jul 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Extracting falsifiable predictions from sloppy models.

Ryan N Gutenkunst1, Fergal P Casey, Joshua J Waterfall

  • 1Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA. rng7@cornell.edu

Annals of the New York Academy of Sciences
|October 11, 2007
PubMed
Summary

Model sloppiness complicates predictions from complex nonlinear models. Understanding this parameter sensitivity is key for selecting optimal data and improving computational analysis for reliable scientific modeling.

Related Experiment Videos

Last Updated: Jul 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Computational modeling
  • Scientific validation

Background:

  • Nonlinear multiparameter models are crucial in many scientific fields.
  • Validating these models relies on making successful, falsifiable predictions.
  • Model 'sloppiness' — extreme parameter sensitivity — complicates prediction extraction.

Purpose of the Study:

  • To explain how model sloppiness impacts data selection for model constraints.
  • To highlight the dangers of linear uncertainty approximations with sloppy models.
  • To address computational challenges in uncertainty analysis and propose communication standards.

Main Methods:

  • Analysis of parameter sensitivities in nonlinear models.
  • Evaluation of data requirements for constraining sloppy models.
  • Review of uncertainty quantification techniques, including Monte Carlo methods.

Main Results:

  • Sloppiness dictates which data are most informative for constraining model predictions.
  • Linear approximations of uncertainty are unreliable for sloppy models.
  • Monte Carlo uncertainty analyses face computational hurdles due to sloppiness.

Conclusions:

  • Addressing model sloppiness is essential for robust scientific predictions.
  • Improved methods for handling parameter uncertainty are needed.
  • Clearer standards for communicating complex model properties are proposed.