Bayesian models are better 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. Our findings revealed that Bayesian hypothesis testing exhibited superior accuracy in identifying evidence for differences compared to the posthoc test, which tended to underestimate the existence of such differences. These results align with a substantial body of previous research highlighting the advantages of Bayesian over frequentist models, thereby emphasizing the need for methodological reform. In conclusion, our study supports the assertion that Bayesian models are more suitable for investigating differences in small datasets of phonetic and/or linguistic data, suggesting that researchers in these fields may find greater reliability in utilizing such models for their analyses.