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Related Experiment Video

Updated: Nov 11, 2025

Perspectives on Neuroscience

Perspectives on Neuroscience

Published on: July 31, 2007

Letter to the Editor.

Thaddeus Tarpey¹, Eva Petkova¹

¹Department of Population Health, New York University.

The American Statistician

|March 25, 2021

Summary

This summary is machine-generated.

This study illustrates aliasing in statistical modeling using beta and normal distributions. An infinite mixture model, inspired by placebo response, provides a new example of this phenomenon.

Keywords:

aliasing convolution mixtures placebo effect

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Area of Science:

Statistics
Probability Theory
Statistical Modeling

Background:

Aliasing is a phenomenon where different models can produce similar results.
Previous work by Hutson and Vexler (2018) demonstrated aliasing with beta and normal distributions.
Modeling placebo response presents challenges in distinguishing true treatment effects from other factors.

Purpose of the Study:

To present a novel illustration of aliasing.
To utilize beta and normal distributions in demonstrating aliasing.
To explore the application of an infinite mixture model inspired by placebo response data.

Main Methods:

The study employs an infinite mixture model.
The model incorporates both beta and normal distributions.
The methodology is inspired by the complexities of placebo response modeling.

Main Results:

A new example of aliasing involving beta and normal distributions is presented.
The infinite mixture model successfully illustrates the aliasing phenomenon.
The findings highlight potential pitfalls in statistical inference when distributions overlap.

Conclusions:

Aliasing can occur with beta and normal distributions, even within complex models.
Infinite mixture models offer a framework for understanding aliasing in practical scenarios.
Careful model specification is crucial when analyzing data with potential for aliasing, such as placebo response.