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Reliability and Validity

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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Updated: Nov 11, 2025

One Dimensional Turing-Like Handshake Test for Motor Intelligence

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

When we can trust computers (and when we can't).

Peter V Coveney^1,2, Roger R Highfield³

¹Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|March 29, 2021

Summary

This summary is machine-generated.

Computational modeling is powerful for theory-grounded science but faces challenges in complex systems. Ensuring reliability requires robust validation, verification, and uncertainty quantification for computational findings.

Keywords:

artificial intelligence big data machine learning uncertainty quantification validation verification

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Area of Science:

Computational science
Scientific modeling
Digital computation

Background:

Increasing computational power fuels expectations for solving complex scientific problems.
The reliability and reproducibility of computational science are critical for scientific advancement.

Purpose of the Study:

To explore the limits and capabilities of computational modeling across diverse scientific domains.
To assess the role of validation, verification, and uncertainty quantification in computational findings.
To discuss the challenges posed by big data and machine learning in complex systems.

Main Methods:

Exploration of computational modeling's effectiveness in theory-grounded versus complex systems.
Analysis of requirements for trust in computer-generated scientific results.
Discussion of limitations of digital computation for certain natural phenomena.

Main Results:

Computational modeling is powerful in simple, theory-grounded science and engineering.
Complex systems (e.g., biology, medicine, social sciences) present challenges to computational objectivity and reproducibility.
Big data and machine learning can create an illusion of objectivity without true explanatory power.

Conclusions:

Availability of code, data, documentation, and rigorous validation/verification are essential for trust in computational science.
Over-reliance on digital computation may be tempered by a renewed emphasis on analogue methods.
Addressing reproducibility challenges in computational science is crucial for reliable scientific discovery.