Ensuring statistics have power

Guidance for designing, reporting and acting on electricity demand reduction and behaviour change programs

Dados Bibliográficos

ID13068012
AutoresBen Anderson (0000-0002-9541-6142, University of Otago, autor correspondente), Tom Rushby (University of Southampton), A S Bahaj (0000-0002-0043-6045, University of Southampton), P A B James (0000-0002-2694-7054, University of Southampton)
Ano2019
Volume59
Páginas101260-101260
Data de publicação2019-09-21
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEnergy Research & Social Science (JOURNAL)
Identificadores do periódicoISSN: 2214-6296 • E-ISSN: 2214-6326
EditoraElsevier BV (PUBLISHER)
DOI10.1016/j.erss.2019.101260
OpenAlexW2973301898
IdiomaEN
Citações recebidas1
Referências citadas17

In this paper we address ongoing confusion over the meaning of statistical significance and statistical power in 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 the p-value bathwater

Actuarial science · Business · Context (archaeology · Economics · Energy (signal processing · Management science · Meaning (existential · Observational study · Operations management · Risk analysis (engineering · Sample (material · Sample size determination · Statistical power · Statistics · Building Energy and Comfort Optimization · Computer Science · Economic and Environmental Valuation · Energy Efficiency and Management · Mathematics · Psychology