Kinds of Science

2019 ◽  
pp. 86-113
Author(s):  
Bradley E. Alger
Keyword(s):  
Big Data ◽  
Hypothesis Testing ◽  
Basic Science ◽  
Karl Popper ◽  
Big Science ◽  
Discovery Science ◽  
Small Science

This chapter reviews distinctions between kinds of science, which is especially relevant to the book’s topic because it is an area that Karl Popper did not consider in detail. This creates a problem since critics of the hypothesis often do not distinguish between true hypothesis-based science and other kinds that don’t depend on hypotheses, and the traditional divisions of science miss the main points. The chapter distinguishes among several modern kinds of science including Big Science/Small Science and how they relate to Big Data and Little Data, and why Discovery Science is different from hypothesis-testing science. It separates “exploratory” from “confirmatory” studies and explains why this terminology can create confusion in trying to understand science. The differences between applied and basic science are genuine and meaningful because these two kinds of science have different goals and apply different, though overlapping, standards to achieve their goals.

Big Science—Small Science

1992 ◽  
pp. 133-138
Author(s):  
Frederick Seitz
Keyword(s):  
Big Science ◽  
Small Science

Small Science: Big Science Will Prevail

Science ◽  
2012 ◽  
Vol 338 (6109) ◽  
pp. 882-883
Author(s):  
L. Siminovitch
Keyword(s):  
Big Science ◽  
Small Science

Big data meets big science

2014 ◽  
Vol 57 (7) ◽  
pp. 13-15 ◽  
Author(s):  
Alex Wright
Keyword(s):  
Big Data ◽  
Big Science

Big Science and Big Data in Biology: From the International Geophysical Year through the International Biological Program to the Long Term Ecological Research (LTER) Network, 1957––Present

2010 ◽  
Vol 40 (2) ◽  
pp. 183-224 ◽  
Author(s):  
Elena Aronova ◽  
Karen S. Baker ◽  
Naomi Oreskes
Keyword(s):  
Big Data ◽  
Ecological Research ◽  
Big Science ◽  
International Geophysical Year ◽  
International Biological Program ◽  
Long Term Ecological Research ◽  
International Geophysical ◽  
Biological Program ◽  
International Biological

This paper discusses the historical connections between two large-scale undertakings that became exemplars for worldwide data-driven scientific initiatives after World War II: the International Geophysical Year (1957––1958) and the International Biological Program (1964––1974). The International Biological Program was seen by its planners as a means to promote Big Science in ecology. As the term Big Science gained currency in the 1960s, the Manhattan Project and the national space program became paradigmatic examples, but the International Geophysical Year provided scientists with an alternative model: a synoptic collection of observational data on a global scale. This new, potentially complementary model of Big Science encompassed the field practices of ecologists and suggested a model for the natural historical sciences to achieve the stature and reach of the experimental physical sciences. However, the program encountered difficulties when the institutional structures, research methodologies, and data management implied by the Big Science mode of research collided with the epistemic goals, practices, and assumptions of many ecologists. By 1974, when the program ended, many participants viewed it as a failure. However, this failed program transformed into the Long-Term Ecological Research program. Historical analysis suggests that many of the original incentives of the program (the emphasis on Big Data and the implementation of the organizational structure of Big Science in biological projects) were in fact realized by the program's visionaries and its immediate investigators. While the program failed to follow the exact model of the International Geophysical Year, it ultimately succeeded in providing a renewed legitimacy for synoptic data collection in biology. It also helped to create a new mode of contemporary science of the Long Term Ecological Research (LTER Network), used by ecologists today.

Hypothesis testing in clinical and basic science research

Transfusion ◽  
2009 ◽  
Vol 50 (9) ◽  
pp. 1878-1880
Author(s):  
Lora M. Giangregorio ◽  
Richard J. Cook
Keyword(s):  
Hypothesis Testing ◽  
Basic Science ◽  
Science Research ◽  
Basic Science Research
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