Confidence intervals (CIs) constitute the most popular alternative to widely criticized null hypothesis significance tests. CIs provide more information than significance tests and lend themselves well to visual displays. Although CIs are no better than significance tests when used solely as significance tests, researchers need not limit themselves to this use of CIs. Rather, CIs can be used to estimate the precision of the data, and it is the precision argument that may set CIs in a superior position to significance tests. We tested two versions of the precision argument by performing computer simulations to test how well sample-based CIs estimate a priori CIs. One version pertains to precision of width whereas the other version pertains to precision of location. Using both versions, sample-based CIs poorly estimate a priori CIs at typical sample sizes and perform better as sample sizes increase.

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基于样本的置信区间估计先验误差的准确性

置信区间（CI）构成了广受批评的零假设重要性检验的最受欢迎替代方法。配置项可提供比重要性测试更多的信息，并且很适合视觉显示。尽管仅将CI用作显着性测验并没有比显着性测验好，但是研究人员并不需要将自己局限于CI的这种使用。相反，配置项可用于估计数据的精度，而精度参数可将配置项置于显着性测试的优越位置。我们通过执行计算机仿真来测试基于精度的参数的两个版本，以测试基于样本的CI估计先验CI的程度。一个版本涉及宽度精度，而另一个版本涉及位置精度。使用两个版本