Do we really need confidence intervals in the new statistics?
Do we really need confidence intervals in the new statistics?
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我们真的需要新统计数据的置信区间吗？
DOI：
10.1080/13645579.2018.1525064
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发表时间：
2018
影响因子：
3.3
通讯作者：
Gorard S
中科院分区：
文献类型：
--
作者：
Gorard S
本文比较了置信区间（CI）和称为所需干扰数（NNTD）的敏感性分析在分析以“效应”大小表示的研究结果时的使用。该论文使用了1,000个随机试验的模拟，每个试验最多有1,000个病例，表明这两种方法的结果非常相似，并且每一种方法都是高度可预测的。CI应该是对结果的可能性或不确定性的度量，显示了单独随机抽样变异可能产生的一系列可能的效应大小。NNTD被认为是效应量对任何变异的稳健性的度量，包括缺失数据产生的变异。考虑到它们在这里测试的条件下基本上是等效的和可互换的，本文认为两者都是鲁棒性的真正衡量标准。它的结论是，NNTD是首选，因为它需要更少的假设，更宽容的缺失数据，更容易解释，并直接解决了关键问题的潜在效应大小是否为零或没有。
This paper compares the use of confidence intervals (CIs) and a sensitivity analysis called the number needed to disturb (NNTD), in the analysis of research findings expressed as ‘effect’ sizes. Using 1,000 simulations of randomised trials with up to 1,000 cases in each, the paper shows that both approaches are very similar in outcomes, and each one is highly predictable from the other. CIs are supposed to be a measure of likelihood or uncertainty in the results, showing a range of possible effect sizes that could have been produced by random sampling variation alone. NNTD is supposed to be a measure of the robustness of the effect size to any variation, including that produced by missing data. Given that they are largely equivalent and interchangeable under the conditions tested here, the paper suggests that both are really measures of robustness. It concludes that NNTD is to be preferred because it requires many fewer assumptions, is more tolerant of missing data, is easier to explain, and directly addresses the key question of whether the underlying effect size is zero or not.
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影响因子：
22.4
作者：
ROZEBOOM, WW
通讯作者：
ROZEBOOM, WW
P 值和置信区间：不适合目的？
DOI：
10.1101/180117
发表时间：
2017
期刊：
bioRxiv
影响因子：
--
作者：
P. Pharoah;Michelle R Jones;Siddartha Kar
通讯作者：
Siddartha Kar
影响因子：
1.2
作者：
FALK, R;GREENBAUM, CW
通讯作者：
GREENBAUM, CW
影响因子：
22.4
作者：
Boring, Edwin G.
通讯作者：
Boring, Edwin G.
难以理解的零假设
影响因子：
16.4
作者：
W. Tryon
通讯作者：
W. Tryon