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000 camIi
001 2210080852651
003 OCoLC
005 20190103135245
006 m d
007 cr |||||||||||
008 160302s2016 dcu ob 100 0 eng d
020 a9780309392037qelectronic bk.
020 a0309392039qelectronic bk.
020 z9780309392020
020 z0309392020
035 a(OCoLC)942666190
040 aSCBbengerdaepncSCBdCUSdNdYDXCPd221008
050 aQ180.55.S7
072 aREFx0180002bisacsh
082 a001.422223
245 00 aStatistical challenges in assessing and fostering the reproducibility of scientific results :bsummary of a workshop /cMichelle Schwalbe, rapporteur ; Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Their Applications, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine.
260 aWashington, DC :bthe National Academies Press,c[2016]
300 a1 online resource (xii, 119 pages)
336 atextbtxt2rdacontent
337 acomputerbc2rdamedia
338 aonline resourcebcr2rdacarrier
504 aIncludes bibliographical references.
520 a"Questions about the reproducibility of scientific research have been raised in numerous settings and have gained visibility through several high-profile journal and popular press articles. Quantitative issues contributing to reproducibility challenges have been considered (including improper data measurement and analysis, inadequate statistical expertise, and incomplete data, among others), but there is no clear consensus on how best to approach or to minimize these problems. A lack of reproducibility of scientific results has created some distrust in scientific findings among the general public, scientists, funding agencies, and industries. While studies fail for a variety of reasons, many factors contribute to the lack of perfect reproducibility, including insufficient training in experimental design, misaligned incentives for publication and the implications for university tenure, intentional manipulation, poor data management and analysis, and inadequate instances of statistical inference. The workshop summarized in this report was designed not to address the social and experimental challenges but instead to focus on the latter issues of improper data management and analysis, inadequate statistical expertise, incomplete data, and difficulties applying sound statistic inference to the available data. Many efforts have emerged over recent years to draw attention to and improve reproducibility of scientific work. This report uniquely focuses on the statistical perspective of three issues: the extent of reproducibility, the causes of reproducibility failures, and the potential remedies for these failures"--Publisher's description.
588 aOnline resource; title from PDF title page (EBSCO, viewed March 30, 2016)
650 aResearchxStatistical methodsvCongresses.
650 aResearchxMethodologyvCongresses.
650 aREFERENCE / Questions & Answers2bisacsh
655 aElectronic books.
700 1 aSchwalbe, Michelle,erapporteur
710 aNational Academies of Sciences, Engineering, and Medicine (U.S.).bCommittee on Applied and Theoretical Statistics.
856 3EBSCOhostuhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1204376
938 aEBSCOhostbEBSCn1204376
938 aYBP Library ServicesbYANKn12902984
994 a92bN
Statistical challenges in assessing and fostering the reproducibility of scientific results :summary of a workshop /Michelle Schwalbe, rapporteur ; Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Their Applications, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine
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전자책
서명
Statistical challenges in assessing and fostering the reproducibility of scientific results :summary of a workshop /Michelle Schwalbe, rapporteur ; Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Their Applications, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine
저자명
Schwalbe Michelle rapporteur
발행사항
Washington, DC : the National Academies Press [2016]
형태사항
1 online resource (xii, 119 pages)
주기사항
Includes bibliographical references. / "Questions about the reproducibility of scientific research have been raised in numerous settings and have gained visibility through several high-profile journal and popular press articles. Quantitative issues contributing to reproducibility challenges have been considered (including improper data measurement and analysis, inadequate statistical expertise, and incomplete data, among others), but there is no clear consensus on how best to approach or to minimize these problems. A lack of reproducibility of scientific results has created some distrust in scientific findings among the general public, scientists, funding agencies, and industries. While studies fail for a variety of reasons, many factors contribute to the lack of perfect reproducibility, including insufficient training in experimental design, misaligned incentives for publication and the implications for university tenure, intentional manipulation, poor data management and analysis, and inadequate instances of statistical inference. The workshop summarized in this report was designed not to address the social and experimental challenges but instead to focus on the latter issues of improper data management and analysis, inadequate statistical expertise, incomplete data, and difficulties applying sound statistic inference to the available data. Many efforts have emerged over recent years to draw attention to and improve reproducibility of scientific work. This report uniquely focuses on the statistical perspective of three issues: the extent of reproducibility, the causes of reproducibility failures, and the potential remedies for these failures"Publisher's description.
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