MARC Record from marc_columbia

LEADER: 03295cam a2200361Ii 4500
001 11908628
005 20160623141810.0
008 160331t20162016dcua b 000 0 eng d
019 $a944157195$a944471703
020 $a0309392020
020 $a9780309392020
035 $a(OCoLC)ocn945728834
035 $a(OCoLC)945728834$z(OCoLC)944157195$z(OCoLC)944471703
035 $a(NNC)11908628
040 $aNRC$beng$cNRC$erda$dYDXCP$dAZS$dBTCTA
050 04 $aQ172.5.E77$bS39 2016
100 1 $aSchwalbe, Michelle,$erapporteur.
245 10 $aStatistical challenges in assessing and fostering the reproducibility of scientific results :$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.
264 1 $aWashington, DC :$bthe National Academies Press,$c[2016]
300 $axii, 119 pages :$b1 color illustration ;$c26 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
504 $aIncludes bibliographical references (pages 97-106).
520 1 $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.
650 0 $aErrors, Scientific.
650 0 $aStatistics.
650 0 $aReplication Research.
650 0 $aReproducibility.
650 0 $aSocial sciences.
655 7 $aConference papers and proceedings.$2lcgft
710 2 $aNational Academies of Sciences, Engineering, and Medicine (U.S.).$bCommittee on Applied and Theoretical Statistics,$eissuing body.
852 00 $boff,sci$hQ172.5.E77$iS39 2016g