WikiSysop: Created page with "== Citation == Balanov, A., Bendory, T. and Huleihel, W. 2025. Confirmation bias in Gaussian mixture models. IEEE Trans. on Information Theory. (2025). == Abstract == Confirmation bias, the tendency to interpret information in a way that aligns with one’s preconceptions, can profoundly impact scientific research, leading to conclusions that reflect the researcher’s hypotheses even when the observational data do not support them. This issue is especially critical i..."

2026-02-20T06:35:06Z

Created page with "== Citation == Balanov, A., Bendory, T. and Huleihel, W. 2025. Confirmation bias in Gaussian mixture models. IEEE Trans. on Information Theory. (2025). == Abstract == Confirmation bias, the tendency to interpret information in a way that aligns with one’s preconceptions, can profoundly impact scientific research, leading to conclusions that reflect the researcher’s hypotheses even when the observational data do not support them. This issue is especially critical i..."

New page

== Citation ==

Balanov, A., Bendory, T. and Huleihel, W. 2025. Confirmation bias in Gaussian mixture models. IEEE Trans. on Information Theory. (2025).

== Abstract ==

Confirmation bias, the tendency to interpret information
in a way that aligns with one’s preconceptions, can
profoundly impact scientific research, leading to conclusions that
reflect the researcher’s hypotheses even when the observational
data do not support them. This issue is especially critical in
scientific fields involving highly noisy observations, such as
cryo-electron microscopy. This study investigates confirmation
bias in Gaussian mixture models. We consider the following
experiment: A team of scientists assumes they are analyzing
data drawn from a Gaussian mixture model with known signals
(hypotheses) as centroids. However, in reality, the observations
consist entirely of noise without any informative structure. The
researchers use a single iteration of the K-means or expectationmaximization
algorithms, two popular algorithms to estimate the
centroids. Despite the observations being pure noise, we show that
these algorithms yield biased estimates that resemble the initial
hypotheses, contradicting the unbiased expectation that averaging
these noise observations would converge to zero. Namely, the
algorithms generate estimates that mirror the postulated model,
although the hypotheses (the presumed centroids of the Gaussian
mixture) are not evident in the observations. Specifically,
among other results, we prove a positive correlation between
the estimates produced by the algorithms and the corresponding
hypotheses. We also derive explicit closed-form expressions of
the estimates for a finite and infinite number of hypotheses.
Furthermore, we provide theoretical and empirical results for
multi-iteration K-means and expectation-maximization, showing
that the bias is persistent even after hundreds of iterations of
these algorithms. This study underscores the risks of confirmation
bias in low signal-to-noise environments, provides insights into
potential pitfalls in scientific methodologies, and highlights the
importance of prudent data interpretation.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/11143585/

== Related software ==

== Related methods ==

== Comments ==

2025Balanov Confirmation - Revision history