Confidence Intervals Better Answers to Better Questions
Confidence Intervals Better Answers to Better Questions
复制标题
DOI：
10.1027/0044-3409.217.1.15
复制
发表时间：
2009-01-01
影响因子：
1.8
通讯作者：
Fidler, Fiona
中科院分区：
文献类型：
--
作者：
Cumming, Geoff;Fidler, Fiona
大多数科学问题都需要定量的答案，理想情况下，一个最佳估计加上关于该估计精度的信息。置信区间（CI）有效地表达了这两种情况。早期的实验心理学家寻求定量的答案，但在过去的世纪，心理学一直被非定量的、二分法的零假设显著性检验（NHST）思维所主导。作者认为，心理学应该重新加入主流科学，提出更好的问题-那些需要定量答案的问题-并使用CI来回答它们。他们解释了CI和一系列思考它们的方法，并使用它们来解释数据，特别是将CI视为预测区间，提供有关复制的信息。他们解释了如何计算平均值，比例，相关性和标准化效应量的CI，并说明了对称和非对称CI。他们还认为，CI提供的信息比p值或基林的p（r ep）值（复制概率）提供的信息更有用。
Most questions across science call for quantitative answers, ideally, a single best estimate plus information about the precision of that estimate. A confidence interval (CI) expresses both efficiently. Early experimental psychologists sought quantitative answers, but for the last half century psychology has been dominated by the nonquantitative, dichotomous thinking of null hypothesis significance testing (NHST). The authors argue that psychology should rejoin mainstream science by asking better questions - those that demand quantitative answers - and using CIs to answer them. They explain CIs and a range of ways to think about them and use them to interpret data, especially by considering CIs as prediction intervals, which provide information about replication. They explain how to calculate CIs on means, proportions, correlations, and standardized effect sizes, and illustrate symmetric and asymmetric CIs. They also argue that information provided by CIs is more useful than that provided by p values, or by values of Killeen's p(r ep), the probability of replication.