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Related Concept Videos

Significant Figures in Calculations00:58

Significant Figures in Calculations

Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:
Data Validation01:03

Data Validation

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.
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Numerical Calculations01:24

Numerical Calculations

In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
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Data Validation

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.
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Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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Verification, validation, and confirmation of numerical models in the Earth sciences.

N Oreskes, K Shrader-Frechette, K Belitz

    Science (New York, N.Y.)
    |February 4, 1994
    PubMed
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    Numerical models of natural systems cannot be fully verified or validated due to their inherent complexity and non-unique results. While models can be confirmed by matching predictions to observations, this confirmation is always partial, highlighting their heuristic value.

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    Area of Science:

    • Environmental modeling
    • Computational science
    • Philosophy of science

    Background:

    • Numerical models are widely used to simulate natural systems.
    • The rigorous verification and validation of these models are crucial for their reliable application.
    • Challenges exist in establishing the absolute accuracy of models representing complex, open systems.

    Purpose of the Study:

    • To critically evaluate the concepts of verification and validation as applied to numerical models of natural systems.
    • To explore the limitations and implications of model confirmation in scientific practice.
    • To redefine the primary utility of scientific models.

    Main Methods:

    • Logical analysis of the concepts of verification, validation, and confirmation.
    • Examination of the philosophical underpinnings of scientific modeling, including the problem of induction and affirming the consequent.
    • Discussion of the inherent characteristics of natural systems (e.g., openness) and model outputs (e.g., non-uniqueness).

    Main Results:

    • Complete verification and validation of natural system models are logically impossible.
    • Natural systems are open, and model results are often non-unique, precluding absolute certainty.
    • Model confirmation, based on agreement between prediction and observation, is partial and subject to logical fallacies.
    • The predictive value of models remains inherently uncertain.

    Conclusions:

    • Models of natural systems can only be evaluated in relative terms, not absolutely validated.
    • The primary value of numerical models lies in their heuristic function—aiding understanding and inquiry—rather than definitive truth.
    • Scientific practice must acknowledge the inherent limitations in model certainty and focus on their role in advancing knowledge.