The end of theory: The data deluge makes the scientific method obsolete

By C. Anderson. 2008

1,511 citations recorded by the source corpus.

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “To capture this excitement, some have even referred to the rise of data science in scientiﬁc disciplines as “the end of theory” [13], the idea being that, the increasingly large amounts of data makes it possible to build actionable models without using scientiﬁc theories.”
Critical data studies: An introduction (2016, background): “Some arguments in favor of data-intensive studies situate Big Data science as capable of overthrowing theory (Anderson, 2008) and providing fine-grained analyses that no longer require the critical eye of postpositivist thinking.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “In the light of Google Flu Trends’ initial success (Ginsberg et al., 2009), the provocative essay “The End of Theory” (Anderson, 2008) sparked intense debates by saying: “Who knows why people do what they do?”
Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb (2013, background): “…limited analyses of the geoweb and the now-popularized, simplistic visions of big data as an atheoretical solution to understanding the spatial dimensions of everyday life that are increasingly well documented on the geoweb (see Anderson 2008 for the most notable example of this kind of thinking).”
Big Data consumer analytics and the transformation of marketing (2016, neutral): “The analytical capability associated with Big Data should ben-e ﬁ t inductive reasoning as such analysis enables researchers to observe phenomena and mathematically identify patterns hidden in Big Data using algorithms without relying on existing knowledge (Anderson, 2008; Lycett, 2013).”
Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’ (2014, background): “As these datasets grow exponentially, researchers, politicians, and the private sector have begun to focus on how ‘big data’ might allow potentially unprecedented insights into our world (Hey and Trefethen, 2003; Anderson, 2008; Floridi, 2012).”
CRITICAL QUESTIONS FOR BIG DATA (2012, background): “( 2008) Do numbers speak for themselves?”
Big Data, new epistemologies and paradigm shifts (2014, background): “The explosion in the production of Big Data, along with the development of new epistemologies, is leading many to argue that a data revolution is underway that has far reaching consequences to how knowledge is produced, business conducted, and governance enacted (Anderson, 2008; Bollier, 2010; Flori”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, methodology): “…trajectories in data science and the methodologies in science, there is an on-going controversy whether the traditional scientiﬁc method is obsolete under the irruption of data science [58], [59], or whether data science methodologies should learn more from the general scientiﬁc method [60], [61].”
Critical Data Studies: A dialog on data and space (2016, background): “A second historical parallel with Big Data is the claim made by early statisticians about the end of causality, as occurred again in 2008 (Anderson, 2008)—in 1838 the London Statistical Society announced that the ﬁeld of statistics was epistemologically diﬀerent from political because ‘‘it does not…”
Sentiment Analysis in Tourism: Capitalizing on Big Data (2019, background): “…a final answer to such challenging questions, it will examine tourism-specific material to further explore whether Big Data is merely a continuation of inductive science (Fricke, 2013) or whether it is the ‘end of theory’ and constitutes a radically new paradigm (Anderson, 2008, Kitchin, 2014).”
Uncovering the Challenges in Collection, Sharing and Documentation: The Hidden Data of Social Media Research? (2021, background): “Recently, and particularly in reaction to claims that Big Data may make many traditional approaches to studying human behavior obsolete (Anderson 2008), critical position papers have emerged that question different aspects of current Big Data analyses in social media research.”
Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “…traditional scientific model of research (which often involves constructing hypotheses based on guesses and then deductively testing the hypotheses using carefully sampled data (e.g., Gregor & Klein, 2014)) in a data-abundant environment associated with big data (Anderson, 2008; Kitchin, 2014b).”