Observe, hypothesize, test, repeat: Luttrell, Petty, and Xu (2017) demonstrate good science
Leigh Ann Vaughn; Christopher R. Chartier; Mitch Brown; Donald Sacco; Ravin Alaei; Brian A. Nosek; Nicholas Rule; Jennifer Joy-Gaba · 2016
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Many Labs 3 (Ebersole et al., 2016) failed to replicate a classic finding from the Elaboration Likelihood Model of persuasion (Cacioppo, Petty, & Morris, 1983; Study 1). Petty and Cacioppo (2016) noted possible limitations of the Many Labs…
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: CORE