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.

翻译：尽管已有许多研究直接比较频率学派模型与贝叶斯模型的结果，但本研究通过考察这两类模型在包含语音数据的小数据集中的表现，提出了新的视角。具体而言，我们采用混合效应模型与贝叶斯回归模型，探究单语人群与双语人群所发元音声学值的差异。研究结果表明，与事后检验相比，贝叶斯假设检验在识别差异证据方面具有更高准确性，而事后检验往往低估了差异的存在。这些结果与大量先前研究一致，这些研究强调了贝叶斯模型相较频率学派模型的优势，从而凸显了方法学改革的必要性。总之，本研究支持贝叶斯模型更适用于探究语音和/或语言数据小数据集差异的论断，并建议这些领域的研究者采用此类模型进行分析可获得更高可靠性。