Litcius/Paper detail

Bayesian Model Comparison and Significance: Widespread Errors and How to Correct Them

Daniel Thorngren, David K. Sing, Sagnick Mukherjee

2026The Astrophysical Journal Supplement Series11 citationsDOIOpen Access PDF

Abstract

Abstract Bayes factors have become a popular tool in exoplanet spectroscopy for testing atmosphere models against one another. We show that the commonly used method for converting these values into significance “sigmas” is invalid. The formula is neither justified nor recommended by its original paper, and overestimates the confidence of results. We use simple examples to demonstrate the invalidity and prior sensitivity of this approach. We review the standard Bayesian interpretation of the Bayes factor as an odds ratio and recommend its use in conjunction with the Akaike information criterion or Bayesian predictive information criterion simplified in future analyses (Python implementations are included). As a concrete example, we refit the WASP-39 b NIRSpec transmission spectrum to test for the presence of SO 2 . The prevalent, incorrect significance calculation gives 3.67 σ , whereas the standard Bayesian interpretation yields a null model probability <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>p</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi class="MJX-tex-calligraphic" mathvariant="script">B</mml:mi> <mml:mo stretchy="false">∣</mml:mo> <mml:mi>y</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.0044</mml:mn> </mml:math> . Surveying the exoplanet atmosphere literature, we find widespread use of the erroneous formula. In order to avoid overstating observational results and estimating observation times too low, the community should return to the standard Bayesian interpretation.

Topics & Concepts

Bayes factorBayesian probabilityInformation CriteriaBayes' theoremComputer scienceStatisticsFrequentist probabilityBayesian statisticsStatistical hypothesis testingNull hypothesisEconometricsBayesian inferenceMachine learningMathematicsInterpretation (philosophy)Standard deviationAkaike information criterionRange (aeronautics)AlgorithmSensitivity (control systems)Bayesian information criterionArtificial intelligenceNull (SQL)Data miningStatistical modelBayesian experimental designBayesian averageStandard errorResidualBayes' ruleOddsPrior probabilityModel selectionBayesian hierarchical modelingStellar, planetary, and galactic studiesAstrophysics and Star Formation StudiesAstronomy and Astrophysical Research