High variability is result of complex data workflows
Previous studies in the fields of psychology, cancer biology and behavioral economics revealed many failures in the attempt to reproduce methodologies and mirror results. The TAU researchers used an approach known as "Many Analysts," in which many researchers analyzed the same dataset to test variability in the analysis outcomes, explains study co-lead author Dr. Tom Schonberg of the Department of Neurobiology at TAU's George S. Wise Faculty of Life Sciences and TAU's Sagol School of Neuroscience.
A new Tel Aviv University-led study published on May 20 offers new evidence that the complexity of contemporary analytical methods in science contributes to the variability of research outcomes. "The variability in outcomes demonstrated in this study is an inherent part of the complex process of obtaining scientific results, and we must understand it in order to know how to tackle it," he adds. "Science is conducted by humans, and there is no way to escape variability. But we must acknowledge this in order to self-correct and attain the most reliable answers."
Dr. Schonberg believes the findings can help scientists advance their methodology and improve the quality of their analyses in the future.
Data is a collection of numbers or symbols. Statistics is a systematic methodology which is used to view the nature of scattered and uncertain data, and to extract a part from large data then view its nature, speculate on the nature of the original big data, etc.
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