Institute of Health Policy and Management, Seoul National University Medical Research Center, Jongno-gu, Seoul, Republic of Korea.
Med Decis Making. 2024 Nov;44(8):890-899. doi: 10.1177/0272989X241275191. Epub 2024 Sep 18.
It is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving.
In a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods-strict rounding, loose rounding, absolute deviation, and 50-Split-were used to evaluate participants' estimations.
The NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems.
The NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks.
The natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups.This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods.The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method.Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.
已有研究证实,在贝叶斯推理中,自然频率(NF)格式比条件概率(CP)格式更具认知优势。然而,实证研究表明,NF 促进效应可能仅限于特定的人群。与之前的研究不同,这些研究仅关注少数通过单一评分方法评估的贝叶斯推理问题,使用各种评分指标评估多个贝叶斯问题至关重要。本研究还探讨了计算能力对贝叶斯推理的影响,并使用问题解决中的数值信息评估了非贝叶斯认知策略。
在一项基于网络的实验调查中,175 名韩国成年人被随机分配到 2 个格式组之一(NF 组与 CP 组)。完成计算能力量表后,要求参与者估计 4 个贝叶斯推理问题,并记录他们在解决问题过程中使用的数值信息。使用严格四舍五入、宽松四舍五入、绝对偏差和 50-分割 4 种评分方法评估参与者的估计值。
除绒毛膜绒毛取样测试问题使用 50-分割方法评估时,NF 格式在所有问题上的表现均优于 CP 格式。此外,计算能力水平对贝叶斯推理有显著影响;计算能力较高的参与者表现更好。此外,参与者使用了各种受格式和问题性质影响的非贝叶斯策略。
NF 促进效应在多个贝叶斯问题和评分方法中均得到一致观察。计算能力较高的个体从 NF 格式中获益更多。格式和特定任务的性质不同,非贝叶斯策略的使用也有所不同。
与条件概率(CP)格式相比,自然频率(NF)格式已被证明有助于理解医学检测结果,但一些研究报告称,这种优势要么不存在,要么仅限于特定群体。本研究旨在使用各种贝叶斯问题和多种评分方法复制先前的实证研究。除了使用 50-分割评分方法评估绒毛膜绒毛取样测试问题时,NF 格式通过所有评分方法促进了对所有贝叶斯问题的医学检测结果的理解。计算能力较高的参与者比计算能力较低的参与者表现出更好的贝叶斯推理能力。特别是,较高的计算能力在 NF 格式中比在较低的计算能力中获益更多。此外,根据任务的格式和性质,公众倾向于使用各种非贝叶斯推理策略。