Literature DB >> 9729547

Fundamental concepts in statistics: elucidation and illustration.

D Curran-Everett1, S Taylor, K Kafadar.   

Abstract

Fundamental concepts in statistics form the cornerstone of scientific inquiry. If we fail to understand fully these fundamental concepts, then the scientific conclusions we reach are more likely to be wrong. This is more than supposition: for 60 years, statisticians have warned that the scientific literature harbors misunderstandings about basic statistical concepts. Original articles published in 1996 by the American Physiological Society's journals fared no better in their handling of basic statistical concepts. In this review, we summarize the two main scientific uses of statistics: hypothesis testing and estimation. Most scientists use statistics solely for hypothesis testing; often, however, estimation is more useful. We also illustrate the concepts of variability and uncertainty, and we demonstrate the essential distinction between statistical significance and scientific importance. An understanding of concepts such as variability, uncertainty, and significance is necessary, but it is not sufficient; we show also that the numerical results of statistical analyses have limitations.

Mesh:

Year:  1998        PMID: 9729547     DOI: 10.1152/jappl.1998.85.3.775

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  10 in total

1.  Regulation of chemokine expression by NaCl occurs independently of cystic fibrosis transmembrane conductance regulator in macrophages.

Authors:  Amanda G Kostyk; Karen M Dahl; Murry W Wynes; Laurie A Whittaker; Daniel J Weiss; Roberto Loi; David W H Riches
Journal:  Am J Pathol       Date:  2006-07       Impact factor: 4.307

2.  Statistical considerations in reporting cardiovascular research.

Authors:  Merry L Lindsey; Gillian A Gray; Susan K Wood; Douglas Curran-Everett
Journal:  Am J Physiol Heart Circ Physiol       Date:  2018-07-20       Impact factor: 4.733

3.  Statistical considerations for occupational and environmental physiology.

Authors:  Douglas Curran-Everett
Journal:  Temperature (Austin)       Date:  2019-07-08

4.  The null hypothesis significance test in health sciences research (1995-2006): statistical analysis and interpretation.

Authors:  Luis Carlos Silva-Ayçaguer; Patricio Suárez-Gil; Ana Fernández-Somoano
Journal:  BMC Med Res Methodol       Date:  2010-05-19       Impact factor: 4.615

5.  Consequences of common data analysis inaccuracies in CNS trauma injury basic research.

Authors:  Darlene A Burke; Scott R Whittemore; David S K Magnuson
Journal:  J Neurotrauma       Date:  2013-05-15       Impact factor: 5.269

Review 6.  Standard deviation and standard error of the mean.

Authors:  Dong Kyu Lee; Junyong In; Sangseok Lee
Journal:  Korean J Anesthesiol       Date:  2015-05-28

7.  Poor statistical reporting, inadequate data presentation and spin persist despite editorial advice.

Authors:  Joanna Diong; Annie A Butler; Simon C Gandevia; Martin E Héroux
Journal:  PLoS One       Date:  2018-08-15       Impact factor: 3.240

8.  Matrix Polysaccharides and SiaD Diguanylate Cyclase Alter Community Structure and Competitiveness of Pseudomonas aeruginosa during Dual-Species Biofilm Development with Staphylococcus aureus.

Authors:  Su Chuen Chew; Joey Kuok Hoong Yam; Artur Matysik; Zi Jing Seng; Janosch Klebensberger; Michael Givskov; Patrick Doyle; Scott A Rice; Liang Yang; Staffan Kjelleberg
Journal:  mBio       Date:  2018-11-06       Impact factor: 7.867

9.  Commentary: childhood cancer near nuclear power stations.

Authors:  Ian Fairlie
Journal:  Environ Health       Date:  2009-09-23       Impact factor: 5.984

10.  What to use to express the variability of data: Standard deviation or standard error of mean?

Authors:  Mohini P Barde; Prajakt J Barde
Journal:  Perspect Clin Res       Date:  2012-07
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.