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Related Experiment Video

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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

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Published on: September 23, 2025

Circular analysis in complex stochastic systems.

Angelo Valleriani¹

¹Max Planck Institute of Colloids and Interfaces, Department of Theory and Bio-Systems, Potsdam, 14424, Germany.

Scientific Reports

|December 15, 2015

Summary

This summary is machine-generated.

Selecting data based on outcomes creates flawed scientific models. This bias, particularly in stochastic processes, leads to models inconsistent with physical reality, even with macroscopic observations.

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Area of Science:

Physics
Statistics
Data Science

Background:

Models are crucial for understanding complex systems.
Selecting data based on outcomes can introduce unintended bias.
Stochastic processes require careful consideration of observational methods.

Purpose of the Study:

To highlight the danger of outcome-based observation selection in model building.
To explain the mechanism by which this bias affects stochastic processes.
To identify why macroscopic observations can be particularly susceptible.

Main Methods:

Conceptual analysis of observational bias in modeling.
Explanation of conditioning effects in stochastic processes.
Discussion of self-consistency in model development.

Main Results:

Outcome-based selection leads to models inconsistent with physical reality.
Conditioning on future outcomes biases transition probabilities in stochastic processes.
Models derived solely from macroscopic observations are prone to this fallacy.

Conclusions:

Researchers must avoid selecting data based on desired outcomes.
Careful methodology is needed to prevent outcome-induced bias in scientific models.
Understanding this fallacy is key to building robust and accurate scientific models.