How to Make More Published Research True

By John P. A. Ioannidis. PLoS Medicine, 2014

701 citations recorded by the source corpus.

In a 2005 paper that has been accessed more than a million times, John Ioannidis explained why most published research findings were false. Here he revisits the topic, this time to address how to improve matters. Please see later in the article for the Editors' Summary

Subfields: Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e1767451f835739003982ff38be99aa4dee76cbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

In Statistics and Probability

Citations in the corpus

On doing better science: From thrill of discovery to policy implications (2017, background): “001 (Johnson, Payne, Wang, Asher, & Mandal, in press), which may help lower false positive findings (Ioannidis, 2014).”
Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles (2019, background): “[99] and there has to be clinical impact or utility.”
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness (2020, background): “Reproducibility of research has become a growing concern across many scientific domains, 53 54 and in the ML/AI field, access to the underlying code and raw data are central to preventing and mitigating reproducibility concerns.”
Recommendations for Replication Research in Special Education (2016, background): “To counter this perception, a number of reforms may help to promote a replication culture (Ioannidis, 2014) in special education, such as (a) journals adopting policies to encourage submission and publication of replication studies regardless of whether they reproduce the findings of the original…”
Statistical Rituals: The Replication Delusion and How We Got There (2018, background): “One drastic proposal to revamp the current system is that only publications whose findings are replicated, not publication per se, should count in the future and that large grants should count not positively but negatively, unless the recipient delivers proportionally high-quality science (Ioannidis”
Reproducibility: Designing a rigorous microscopy experiment: Validating methods and avoiding bias (2019, positive): “Bias, blinding, and randomization Experimenter bias Humans are prone to many types of unconscious bias, and scientists are no exception (Nickerson, 1998; Ioannidis, 2014; Holman et al., 2015; Nuzzo, 2015; Lazic, 2016; Munafò et al., 2017).”
A “datathon” model to support cross-disciplinary collaboration (2016, background): “If half of reported research is indeed unreliable (3), those who read the literature are not only wasting half their time, they are actually poisoning half their time with falsehoods.”
A visual analytics approach to understanding cycling behaviour (2012, neutral): “The most important non-dimensional numbers controlling the aerodynamic droplet breakup are the As breakup process is not instantaneous, the breakup timescale proposed by Nicholls and Ranger [2] can be used as a convenient non-dimensionalisation parameter for understanding the temporal development…”
The Power of Bias in Economics Research (2017, background): “Many options are available to move forward, as summarised by Ioannidis (2014) and Maniadis et al. (2015).”
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances (2018, neutral): “For clinical NLP method development to become more integral in clinical outcomes research, there is a need to develop evaluation workbenches that can be used by clinicians to better understand the underlying parts of an NLP system and its impact on outcomes.”