Erratum: Reinventing Biostatistics Education for Basic Scientists (PLoS Biology (2016) 14:4 (e1002430) DOI: 10.1371/journal.pbio.1002430);Reinventing Biostatistics Education for Basic Scientists

PLoS Biology

Volumn 14, Issue 4, 2016, Pages

  Weissgerber, Tracey L ^a   Garovic, Vesna D ^a   Milin Lazovic, Jelena S ^b   Winham, Stacey J ^a   Obradovic, Zoran ^c   Trzeciakowski, Jerome P ^d   Milic, Natasa M ^a,b  

^a MAYO CLINIC   (United States)

^b UNIVERSITY OF BELGRADE   (Serbia)

^c TEMPLE UNIVERSITY   (United States)

^d TEXAS A AND M HEALTH SCIENCE CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOSTATISTICS; EDUCATION; HUMAN; HUMAN EXPERIMENT; SCIENTIST; SCIENCE;

BIOSTATISTICS; SCIENCE;

EID: 84964899070     PISSN: 15449173     EISSN: 15457885     Source Type: Journal    
DOI: 10.1371/journal.pbio.1002492     Document Type: Erratum

References (27)

1. Strasak AM, Zaman Q, Marinell G, Pfeiffer KP, Ulmer H, The use of statistics in medical research: A comparison of the New England Journal of Medicine and Nature Medicine. The American Statistician. 2007;61: 47–55.
2. Baker D, Lidster K, Sottomayor A, Amor S, Two years later: journals are not yet enforcing the ARRIVE guidelines on reporting standards for pre-clinical animal studies. PLoS Biol. 2014;12: e1001756. doi: 10.1371/journal.pbio.100175624409096
3. Lazic SE, The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?BMC Neurosci. 2010;11: 5. doi: 10.1186/1471-2202-11-520074371
4. Weissgerber T, Milic N, Winham S, Garovic VD, Beyond Bar Graphs: Time for a New Data Presentation Paradigm. PLoS Biol. 2015;13: e1002128. doi: 10.1371/journal.pbio.100212825901488
5. Garcia-Berthou E, Alcaraz C, Incongruence between test statistics and P values in medical papers. BMC medical research methodology. 2004;4: 13. 15169550
6. Nuijten MB, Hartgerink CH, van Assen MA, Epskamp S, Wicherts JM, The prevalence of statistical reporting errors in psychology (1985–2013). Behavior research methods. 2015; doi: 10.3758/s13428-015-0664-2
7. Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD, The extent and consequences of p-hacking in science. PLoS Biol. 2015;13: e1002106. doi: 10.1371/journal.pbio.100210625768323
8. Halsey LG, Curran-Everett D, Vowler SL, Drummond GB, The fickle P value generates irreproducible results. Nature methods. 2015;12: 179–185. doi: 10.1038/nmeth.328825719825
9. Ioannidis JP, Why most published research findings are false. PLoS Med. 2005;2: e124. 16060722
10. Vaux DL, Research methods: Know when your numbers are significant. Nature. 2012;492: 180–181. doi: 10.1038/492180a23235861
11. Reproducing our irreproducibility. Nature. 2013;496: 398.
12. Collins FS, Tabak LA, Policy: NIH plans to enhance reproducibility. Nature. 2014;505: 612–613. 24482835
13. Take the long view. Nat Med. 2016;22: 1. doi: 10.1038/nm.403326735395
14. Oster RA, Lindsell CJ, Welty LJ, Mazumdar M, Thurston SW, Rahbar MH, et al. Assessing Statistical Competencies in Clinical and Translational Science Education: One Size Does Not Fit All. Clinical and translational science. 2014;8: 32–42. doi: 10.1111/cts.1220425212569
15. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nature reviews Neuroscience. 2013;14: 365–376. doi: 10.1038/nrn347523571845
16. Holman C, Piper SK, Grittner U, Diamantaras AA, Kimmelman J, Siegerink B, et al. Where Have All the Rodents Gone? The Effects of Attrition in Experimental Research on Cancer and Stroke. PLoS Biol. 2016;14: e1002331. doi: 10.1371/journal.pbio.100233126726833
17. Charan J, Kantharia ND, How to calculate sample size in animal studies?Journal of pharmacology & pharmacotherapeutics. 2013;4: 303–306.
18. Council for the International Organization of Medical Sciences, The International Council for Laboratory and Animal Sciences (2012) International Guiding Principles for Biomedical Research Involving Animals. http://grants.nih.gov/grants/olaw/Guiding_Principles_2012.pdf
19. Fosang AJ, Colbran RJ, Transparency Is the Key to Quality. The Journal of biological chemistry. 2015;290: 29692–29694. doi: 10.1074/jbc.E115.00000226657753
20. Galbraith S, Daniel JA, Vissel B, A study of clustered data and approaches to its analysis. J Neurosci. 2010;30: 10601–10608. doi: 10.1523/JNEUROSCI.0362-10.201020702692
21. Pallmann P, Hothorn LA, Boxplots for grouped and clustered data in toxicology. Archives of Toxicology. 2015; doi: 10.1007/s00204-015-1608-4
22. Haag WG, Abril-Horpel O, Becquerelle SD, Mertz PM, Davis SC, Statistical approach for avoiding pseudoreplication and increasing power in wound-healing studies. Wound Repair Regen. 2011;19: 442–448. doi: 10.1111/j.1524-475X.2011.00693.x21539651
23. Freeberg TM, Lucas JR, Pseudoreplication is (still) a problem. J Comp Psychol. 2009;123: 450–451. doi: 10.1037/a001703119929114
24. Hurlbert SH, Pseudoreplication and the design of ecological field experiments. Ecological Monographs. 1984;54: 187–211.
25. Stanisavljevic D, Trajkovic G, Marinkovic J, Bukumiric Z, Cirkovic A, Milic N, Assessing attitudes towards statistics among medical students: psychometric properties of the Serbian version of the Survey of Attitudes Towards Statistics (SATS). PLoS ONE. 2014;9: e112567. doi: 10.1371/journal.pone.011256725405489
26. Means B, Toyama Y, Murphy R, Baki M, The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record. 2013;115: 030303.
27. Milic N, Trajkovic G, Bukumiric Z, Cirkovic A, Nikolic I, Milin-Lazovic J, et al. Improving education in medical statistics: implementing a blended learning model in the existing curriculum. PLOS ONE. 2016;11: e0148882. doi: 10.1371/journal.pone.014888226859832