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We used Specification Curve Analysis (an innovative statistical technique), to examine how much impact this potential for analytical flexibility has on the scientific conclusions reached in a resulting paper. (6/10)
Short answer: A huge impact! Running over 20,000 analyses on 3 different datasets we found that scientists could have written 1000s of papers finding positive, negative and non-significant associations between well-being and digital tech - it all depends on the analysis (7/10)pic.twitter.com/PdyT16Rmnn
For example, defining digital technology use in different ways affects the results found (8/10)pic.twitter.com/tN2W6SseD2
Or… whether you include good quality controls in the analysis or not. As you see, not having proper controls makes the association between digital technology use and well-being a lot more negative. (9/10)pic.twitter.com/jPZsg0iKNp
The paper powerfully visualises that without pre-registering analysis plans beforehand, analytical bias can allow researchers to tell almost any story with powerful data resources. Is this really what science is supposed to be like? (10/10)pic.twitter.com/yz5rzkki62
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