Introduction: estimation of confidence intervals (CIs) of binomial proportions has been reviewed more than once but the directional interpretation, distinguishing the overestimation from the underestimation, was neglected while the sample size and theoretical proportion variances from experiment to experiment have not been formally taken in account. Herein, we define and apply new evaluation criteria, then give recommendations for the practical use of these CIs. Materials & methods: Google Scholar was used for bibliographic research. Evaluation criteria were (i) one-sided conditional errors, (ii) one-sided local average errors assuming a random theoretical proportion and (iii) expected half-widths of CIs. Results: Wald's CI did not control any of the risks, even when the expected number of successes reached 32. The likelihood ratio CI had a better balance than the logistic Wald CI. The Clopper-Pearson mid-P CI controlled well one-sided local average errors whereas the simple Clopper-Pearson CI was strictly conservative on both one-sided conditional errors. The percentile and basic bootstrap CIs had the same bias order as Wald's CI whereas the studentized CIs and BCa, modified for discrete bootstrap distributions, were less biased but not as efficient as the parametric methods. The half-widths of CIs mirrored local average errors. Conclusion: we recommend using the Clopper-Pearson mid-P CI for the estimation of a proportion except for observed-theoretical proportion comparison under controlled experimental conditions in which the Clopper-Pearson CI may be better.

翻译：材料和方法:谷歌学者用于书目研究,评估标准是:(一) 单向有条件误差,(二) 单向当地平均误差,假设随机的理论误差,(三) 预期的CI的半宽度。结果:Wald's CI没有控制任何风险,即使预期的成功率达到32 。 CI 可能比 Wald CI 更平衡。 Clopper-Pearson 中PCI 控制着当地平均误差,而简单的 Clopper-Pearson CIS 则严格保守两种偏差。百分数和基本靴杆 CIS 可能与Wald's CI 的偏差顺序相同,而CIS 和 CIRC 平均误差则比为CIS