000			02677nam a2200349 i 4500
001			CR9781139161923
003			UkCbUP
005			20240912201713.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110922s2014||||enk o ||1 0|eng|d
020			_a9781139161923 (ebook)
020			_z9781107024151 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA277 _b.B66 2014
082	0	0	_a519.5/4 _223
100	1		_aBookstein, Fred L., _d1947- _eauthor.
245	1	0	_aMeasuring and reasoning : _bnumerical inference in the sciences / _cFred L. Bookstein.
246	3		_aMeasuring & Reasoning
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xxviii, 535 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine generated contents note: Part I. The Basic Structure of a Numerical Inference: 1. Getting started; 2. Consilience as a rhetorical strategy; 3. Abduction and strong inference; Part II. A Sampler of Strategies: 4. The undergraduate course; Part III. Numerical Inference for General Systems: 5. Abduction and consilience in more complicated systems; 6. The singular value decomposition: a family of pattern engines for organized systems; 7. Morphometrics, and other examples; Part IV. What Is to Be Done?: 8. Retrospect and prospect.
520			_aIn Measuring and Reasoning, Fred L. Bookstein examines the way ordinary arithmetic and numerical patterns are translated into scientific understanding, showing how the process relies on two carefully managed forms of argument: • Abduction: the generation of new hypotheses to accord with findings that were surprising on previous hypotheses, and • Consilience: the confirmation of numerical pattern claims by analogous findings at other levels of measurement. These profound principles include an understanding of the role of arithmetic and, more importantly, of how numerical patterns found in one study can relate to numbers found in others. More than 200 figures and diagrams illuminate the text. The book can be read with profit by any student of the empirical nature or social sciences and by anyone concerned with how scientists persuade those of us who are not scientists why we should credit the most important claims about scientific facts or theories.
650		0	_aStatistical hypothesis testing.
776	0	8	_iPrint version: _z9781107024151
856	4	0	_uhttps://doi.org/10.1017/CBO9781139161923
942			_2ddc _cEB
999			_c9504 _d9504