Bayesian Models Are More Sensitive than Frequentist Models in Identifying Differences in Small Datasets Comprising Phonetic Data

While many studies have previously conducted direct comparisons between results obtained from frequentist and Bayesian models, our research introduces a novel perspective by examining these models in the context of a small dataset comprising phonetic data. Specifically, we employed mixed-effects models and Bayesian regression models to explore differences between monolingual and bilingual populations in the acoustic values of produced vowels. The former models are widely utilized in linguistic and phonetic research, whereas the latter offer promising approaches for achieving greater precision in data analysis. Our findings revealed that Bayesian hypothesis testing identified more differences compared to the post hoc test. Specifically, the post hoc test identified differences solely in the F1 of the vowel /a/, whereas the evidence ratios provided strong evidence of differences across multiple vowels and all measured parameters, including F1, F2, F3, and duration. These results may call into question the findings of a large number of studies incorporating frequentist models. In conclusion, our study supports the assertion that different statistical frameworks can lead to divergent interpretations, especially in cases with small sample sizes and complex data structures like those commonly found in phonetics. This can open a discussion about the need for careful methodological considerations and the potential benefits of Bayesian approaches in such situations.