"Correlation is not causation" pic.twitter.com/7TcRlwJB1Q— Ameet Kini, MD, PhD (@AmeetRKini) October 22, 2019
“Data dredging (also data fishing, data snooping, data butchery, and p-hacking) is the misuse of data analysis to find patterns in data that can be presented as statistically significant when in fact there is no real underlying effect. This is done by performing many statistical tests on the data and only paying attention to those that come back with significant results, instead of stating a single hypothesis about an underlying effect before the analysis and then conducting a single test for it.”
Source : Data dredging – Wikipedia
When a study of 42 subjects inspires 34 million people, it’s not unreasonable to go back and check the results.
While our interviewees acknowledge challenges posed by the emergence of big data approaches, they reassert the importance of fundamental tenets of social science research such as establishing causality and drawing on existing theory. They also discussed more pragmatic issues, such as collaboration between researchers from different fields, and the utility of mixed methods.