Rethinking Statistical Significance: A Bayesian Alternative to P-Values in Public Opinion Survey Res

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Rethinking Statistical Significance: A Bayesian Alternative to P-Values in Public Opinion Survey Results
P-values are widely used in public opinion research to assess statistical significance in binary survey data but face criticism for being overly sensitive to sample size, often misinterpreted, and failing to incorporate prior knowledge. These limitations hinder actionable insights crucial for policy-making. Bayesian methods, particularly Bayesian Logistic Regression (BLR), offer a robust alternative by providing probabilistic interpretations and integrating prior knowledge.
This study applies BLR to binary survey data from a Saudi public opinion survey of 1,300 participants, focusing on financial assistance across income groups. Using the rstanarm package in R, the analysis specifies priors, conducts convergence diagnostics (e.g., Rhat), and performs posterior predictive checks. BLR is expected to reveal significant effects overlooked by p-values, provide probabilistic insights, and improve model fit. This approach advances public opinion research by offering a more nuanced and reliable framework for analyzing binary survey data, supporting informed policy decisions.

Bayesian Logistic Regression (BLR)
Public Opinion Research
Binary Survey Data
Statistical Significance
P-values Criticism
Bayesian Methods 

Section on Bayesian Statistical Science