Ten Common Statistical Mistakes to Watch out for When Writing or Reviewing a Manuscript
Broadcast date: September 15, 2023On-demand
Jean-Jacques Orban de Xivry
Jean-Jacques Orban de Xivry is an engineer in applied mathematics and an Associate Professor in the Department of Movement Sciences at KU Leuven. He obtained his PhD in cognitive neuroscience in 2007 from the Université Catholique de Louvain. His post-doctoral research at Johns Hopkins University focused on transcranial magnetic and direct stimulation to investigate the role of the motor cortex in motor adaptation, after which he secured funding to become an independent researcher. His current project focuses on the effect of aging and neurological disease on motor control and learning using a multimodal approach.
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Discover the most common statistical and reporting errors in the scientific literature and learn reliable methods to increase the rigor of your data and its reporting.
In this webinar, you will:
- Discover the benefits of transparent reporting;
- Explore common statistical mistakes and the solutions to them;
- Gain the tools to assess the reliability of your findings and of those reported in the literature.
Some scientific papers are unbelievable as they showcase experimental tour de force that we did not think possible. These papers astound us with their groundbreaking findings and meticulous methodologies.
However, alongside these unbelievably good papers, there are papers that are unbelievable due to the pervasive presence of statistical mistakes that undermine their credibility. Whether due to negligence, lack of expertise, or a rush to publish, these errors cast doubt on the validity of the reported results and conclusions.
In this talk, JJ Orban de Xivry will delve into the ten most common statistical mistakes that plague scientific research (Makin and Orban de Xivry, eLife, 2019).
With a focus on identifying these errors, he will shed light on the detrimental impact they have on the reliability of scientific findings. By identifying and rectifying statistical mistakes, researchers can ensure the foundation of their field of study.
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