Supplemental Material for Human Optional Stopping in a Heteroscedastic World

2021 ◽  
Keyword(s):  
Optional Stopping

scholarly journals Erratum

1989 ◽  
Vol 12 (1) ◽  
pp. 187-187
Author(s):  
Robyn M. Dawes
Keyword(s):  
Sample Size ◽  
Prior Knowledge ◽  
Negative Correlation ◽  
Effect Size ◽  
Critical Appraisal ◽  
Personal Communication ◽  
Optional Stopping ◽  
Incorrect Assertion ◽  
Z Scores ◽  
Random Generators

In my comments in BBS (Random generators, ganzfields, analysis, and theory, 1987, 10:581-82) regarding psi, I mistakenly ascribed to Professor Honorton the position that "good experimenters knew in advance that the assertion in the paper I cited (1985), and in fact regards it as a rather foolish one (personal communication 6/25/88). This incorrect assertion was based on my inference - not his - that the most plausible alternative to optional stopping for the negative correlation between sample size and effect size (and even z-scores) was prior knowledge leading to the necessity of sampling fewer observations when the expectation of the estimated effect size was larger.Honorton, C. (1985) The Ganzfeld psi experiment: A critical appraisal. Journal of Parapsychology 49:51-91.

scholarly journals Does preregistration improve the credibility of research findings?

2020 ◽  
Author(s):  
Mark Rubin
Keyword(s):  
Data Collection ◽  
Multiple Testing ◽  
Research Data ◽  
Public Access ◽  
Research Report ◽  
Selective Reporting ◽  
Optional Stopping ◽  
Research Findings ◽  
Analytical Approaches ◽  
Published Research

Preregistration entails researchers registering their planned research hypotheses, methods, and analyses in a time-stamped document before they undertake their data collection and analyses. This document is then made available with the published research report to allow readers to identify discrepancies between what the researchers originally planned to do and what they actually ended up doing. This historical transparency is supposed to facilitate judgments about the credibility of the research findings. The present article provides a critical review of 17 of the reasons behind this argument. The article covers issues such as HARKing, multiple testing, p-hacking, forking paths, optional stopping, researchers’ biases, selective reporting, test severity, publication bias, and replication rates. It is concluded that preregistration’s historical transparency does not facilitate judgments about the credibility of research findings when researchers provide contemporary transparency in the form of (a) clear rationales for current hypotheses and analytical approaches, (b) public access to research data, materials, and code, and (c) demonstrations of the robustness of research conclusions to alternative interpretations and analytical approaches.

Sign in / Sign up

Export Citation Format

Share Document