同样的数据,不同的分析师:由于生态学和进化生物学中的分析决策,效应大小的变化
Elliot Gould1, Hannah S Fraser2, Timothy H Parker3
1School of Agriculture Food and Ecosystem Sciences, University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia.
BMC biology
|February 6, 2025
概括
研究人员分析相同的生态数据产生了截然不同的结果,质疑科学发现的可靠性. 效应大小和预测的这种变化凸显了生态学和进化生物学中的分析选择的影响.
科学领域:
- 生态学和进化生物学
- 生态研究方法 生态研究方法
- 科学中的统计分析.
背景情况:
- 研究结果的变化往往超过预期的抽样错误.
- 研究人员在统计分析决策中的差异是潜在的原因.
- 之前的研究表明,社会科学中的分析变异性很大,生态学的工作有限.
研究的目的:
- 在生态学和进化生物学中对效应大小的变化和模型预测进行大规模的实证探索.
- 通过使用真实生态数据集,调查研究人员的不同分析决策如何影响研究结果.
主要方法:
- 利用了两个未公布的数据集:一个是蓝 (Cyanistes caeruleus) 兄弟姐妹数量和巢生长,另一个是树草皮和幼苗招募.
- 招募了174个分析团队 (246名分析师) 来解决预先规定的研究问题.
- 收集了蓝数据集的141个可用效果,以及树数据集的85个效果.
主要成果:
- 对于两个数据集,结果中发现了实质性的异质性,具有不同的变化模式.
- 蓝头分析显示了令人信服的负平均效应,但影响大小的差异很大,包括那些跨越显著性值的效应.
- 树分析显示了接近零的平均效应,大多数结果是微弱的负数到正数,尽管存在一些异常值;观察到变量选择和随机效应结构的变化,但与结果偏差没有很强的相关性.
结论:
- 分析变异性显著影响生态和进化研究中的效应大小和预测.
- 该研究强调了结果的相当多样性,尽管数据和研究问题一致.
- 这些发现引发了关于解释公布的生态结果和未来分析实践的关键问题.
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