Ensuring statistics have power: Guidance for designing, reporting and acting on electricity demand reduction and behaviour change programs
Ensuring statistics have power: Guidance for designing, reporting and acting on electricity demand reduction and behaviour change programs
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确保统计数据具有效力：减少电力需求和行为改变计划的设计、报告和行动指南
DOI：
10.1016/j.erss.2019.101260
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发表时间：
2020
影响因子：
6.7
通讯作者：
Anderson B
中科院分区：
文献类型：
--
作者：
Anderson B
在本文中，我们解决了在能源效率和能源需求减少干预研究中统计显著性和统计能力的含义的持续混淆。我们将讨论这些概念在设计研究中的作用，决定从结果中可以推断出什么，从而采取什么后续行动。我们使用新西兰热泵需求响应研究的工作示例来展示如何适当地大小实验和观察研究，这对后续数据分析和随后可以采取的决策的影响。论文随后提出了两套建议。第一部分关注的是统计功率分析和样本设计这一没有争议但似乎可以忽略的问题，这在能源研究文献中经常被忽略。第二个重点是如何报告减少能源需求的研究或试验结果，做出推断，并以适当的方式采取商业或政策导向的决策。因此，本文为负责设计和评估此类研究的研究人员提供了指导；项目经理需要了解什么可以算作证据，为了什么目的，在什么背景下，决策者需要根据证据做出可辩护的商业或政策决策。因此，本文帮助所有这些利益相关者区分对统计显著性的搜索和对可诉证据的要求，从而避免将实质性婴儿与价值洗澡水一起倒掉。
In this paper we address ongoing confusion over the meaning ofstatistical significanceandstatistical powerin energy efficiency and energy demand reduction intervention studies. We discuss the role of these concepts in designing studies, in deciding what can be inferred from the results and thus what course of subsequent action to take. We do this using a worked example of a study of Heat Pump demand response in New Zealand to show how to appropriately size experimental and observational studies, the consequences this has for subsequent data analysis and the decisions that can then be taken. The paper then provides two sets of recommendations. The first focuses on the uncontroversial but seemingly ignorable issue of statistical power analysis and sample design, something regularly omitted in the energy studies literature. The second focuses on how to report energy demand reduction study or trial results, make inferences and take commercial or policy-oriented decisions in a contextually appropriate way. The paper therefore offers guidance to researchers tasked with designing and assessing such studies; project managers who need to understand what can count as evidence, for what purpose and in what context and decision makers who need to make defensible commercial or policy decisions based on that evidence. The paper therefore helps all of these stakeholders to distinguish the search for statistical significance from the requirement for actionable evidence and so avoid throwing the substantive baby out with thep-value bathwater.
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