Schick Robert S, Greenwood Jeremy J D, Buckland Stephen T
Centre for Research into Ecological & Environmental Modelling, The Observatory, Buchanan Gardens, University of St Andrews, St Andrews, Fife, KY16 9LZ Scotland UK.
Marine Geospatial Ecology Lab, Nicholas School of the Environment, Duke University, Durham, NC 27708 USA.
Environ Sci Eur. 2017;29(1):4. doi: 10.1186/s12302-016-0103-8. Epub 2017 Jan 23.
We assess the analysis of the data resulting from a field experiment conducted by Pilling et al. (PLoS ONE. doi: 10.1371/journal.pone.0077193, 5) on the potential effects of thiamethoxam on honeybees. The experiment had low levels of replication, so Pilling et al. concluded that formal statistical analysis would be misleading. This would be true if such an analysis merely comprised tests of statistical significance and if the investigators concluded that lack of significance meant little or no effect. However, an analysis that includes estimation of the size of any effects-with confidence limits-allows one to reach conclusions that are not misleading and that produce useful insights.
For the data of Pilling et al., we use straightforward statistical analysis to show that the confidence limits are generally so wide that any effects of thiamethoxam could have been large without being statistically significant. Instead of formal analysis, Pilling et al. simply inspected the data and concluded that they provided no evidence of detrimental effects and from this that thiamethoxam poses a "low risk" to bees.
Conclusions derived from the inspection of the data were not just misleading in this case but also are unacceptable in principle, for if data are inadequate for a formal analysis (or only good enough to provide estimates with wide confidence intervals), then they are bound to be inadequate as a basis for reaching any sound conclusions. Given that the data in this case are largely uninformative with respect to the treatment effect, any conclusions reached from such informal approaches can do little more than reflect the prior beliefs of those involved.
我们评估了皮林等人(《公共科学图书馆·综合》。doi:10.1371/journal.pone.0077193,5)进行的一项田间实验所产生的数据,该实验旨在研究噻虫嗪对蜜蜂的潜在影响。该实验的重复水平较低,因此皮林等人得出结论,形式上的统计分析会产生误导。如果这样的分析仅仅包括统计显著性检验,并且研究者得出缺乏显著性意味着几乎没有影响的结论,那么情况确实如此。然而,一种包括估计任何影响的大小以及置信区间的分析,能够让人得出不会产生误导且能提供有用见解的结论。
对于皮林等人的数据,我们使用直接的统计分析表明,置信区间通常非常宽,以至于噻虫嗪的任何影响可能很大但却没有统计学显著性。皮林等人没有进行形式上的分析,只是简单检查了数据,并得出数据没有提供有害影响证据的结论,进而得出噻虫嗪对蜜蜂构成“低风险”的结论。
在这种情况下,通过检查数据得出的结论不仅具有误导性,而且在原则上也是不可接受的。因为如果数据不足以进行形式上的分析(或者仅足以提供具有宽置信区间的估计),那么它们必然不足以作为得出任何可靠结论的基础。鉴于在这种情况下的数据在很大程度上对于处理效应并无信息价值,从这种非正式方法得出的任何结论只不过反映了相关人员的先入之见而已。