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Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
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Non-Gaussianity as a data analysis artifact.

Edoardo Milotti1

  • 1Dipartimento di Fisica, Università di Trieste, and I. N. F. N.-Sezione di Trieste, Via Valerio 2, I-34127 Trieste, Italy. Edoardo.Milotti@ts.infn.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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Finite-size effects can introduce non-Gaussianity in data analysis, even in tools designed to be asymptotically Gaussian. Researchers must carefully scrutinize statistical methods when searching for non-Gaussian effects in experiments.

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Area of Science:

  • Physics
  • Statistical Analysis

Background:

  • Non-Gaussian effects are crucial in various physics domains.
  • Numerous experiments aim to detect non-Gaussianity.

Purpose of the Study:

  • To demonstrate how finite-size effects can lead to non-Gaussianity.
  • To highlight the need for rigorous statistical tool evaluation in non-Gaussianity searches.

Main Methods:

  • Analysis of data analysis tools exhibiting asymptotic Gaussian behavior.
  • Investigation of finite-size effects on statistical properties.

Main Results:

  • An insidious form of non-Gaussianity can emerge from finite-size effects.
  • This artifact can occur in statistically robust tools.

Conclusions:

  • Experimental searches for non-Gaussianity require meticulous examination of statistical analysis techniques.
  • Finite-size effects pose a potential challenge in interpreting experimental data for non-Gaussian signatures.