Bayesian models are better than frequentist models in identifying differences in small datasets comprising phonetic data
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ID: 282594
2023
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Abstract
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.
| Reference Key |
georgiou2023bayesian
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| Authors | Georgios P. Georgiou |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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