Response to Howard (2018): Comments on ‘A Meta-Reanalysis of Dream-ESP Studies’
Lance Storm, Adam J. Rock, Simon J. Sherwood, Patrizio Tressoldi, Chris A Roe
University of Derby Online Research Archive. February 8, 2019 DOI: 10.11588/ijodr.2019.1.59265 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractDream-ESP is a form of extrasensory perception in which a sleeping person supposedly gains information about a randomly selected target without using normal senses or logic. A meta-analysis of studies from 1966 to 2016 found significant effects supporting the ESP hypothesis. A critique by Howard (2018) reported much weaker effects after re-analyzing the data using inverse-variance weights. While Howard replicated some findings, other results are disputed. The authors discuss meta-analytic methods, including publication bias and handling outliers, and present re-analyses challenging some of Howard's conclusions.
Study at a glance
| Characteristics | Meta-analysis Peer reviewed |
|---|---|
| Keywords | Dream Meta-analysis Inference Variance accounting Extrasensory perception |
| Key finding | A meta-analysis of dream-ESP studies found significant effects supporting the ESP hypothesis, though a re-analysis using inverse-variance weights yielded weaker effects. |
Abstract
Dream-ESP is a form of extra-sensory perception (ESP) in which a dreaming perceiver ostensibly gains information about a randomly selected target without using the normal sensory modalities or logical inference. We conducted a meta-analysis on dream-ESP studies (dating from 1966 to 2016), and found a number of significant effects indicating support for the ESP hypothesis (Storm et al., 2017). Howard (2018) critiqued our study, and found much weaker effects based on a re-analysis of our data, to which he applied inverse-variance weights to the study values. Although Howard replicated a number of our findings, his other findings can be challenged. We discuss meta-analytic approaches, including the controversial issues of publication bias and what to do with outliers, and we present some re-analyses.