Common misconceptions about data analysis and statistics

Pharmacology Research and Perspectives

Volumn 3, Issue 1, 2015, Pages 1-8

  Motulsky, Harvey J ^a  

^a GraphPad Software   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BAYES THEOREM; BIOMEDICAL ENGINEERING; CALCULATION; DATA ANALYSIS; PRIORITY JOURNAL; REPRODUCIBILITY; SAMPLE SIZE; SCIENTIFIC LITERATURE; STATISTICS; THERAPEUTIC MISCONCEPTION;

EID: 85009733535     PISSN: None     EISSN: 20521707     Source Type: Journal    
DOI: 10.1002/prp2.93     Document Type: Article

References (19)

1. Anonymous (2013). Trouble at the lab. Economist 409: 2013.
2. Begley CG, Ellis LM (2012). Raise standards for preclinical cancer research. Nature 483: 533.
3. Berry DA (2007). The difficult and ubiquitous problems of multiplicities. Pharm Stat 6: 155-160.
4. Cohen J (1994). The earth is round (P < 0.05). Am Psychol 49: 997.
5. Collins FS, Tabak LA (2014). Policy: NIH plans to enhance reproducibility. Nature 505: 612-613.
6. Colquhoun D (2014). An investigation of the false discovery rate and the misinterpretation of P values. Available at http://arxiv.org/abs/1407.5296 (accessed September 16, 2014).
7. FDA (2010). Guidance for industry: adaptive design clinical trials for drugs and biologics. Available at http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf (accessed July 29, 2014).
8. Hankins M 2013. Still not significant. Psychologically flawed april 21, 2013. Available at mchankins.wordpress.com/2013/04/21/still-not-significant-2/ (accessed July 28, 2013).
9. Ioannidis JPA (2005). Why most published research findings are false. PLoS Med 2: e124.
10. Kairalla JA, Coffey CS, Thomann MA, Muller KE (2012). Adaptive trial designs: a review of barriers and opportunities. Trials 13: 145.
11. Kerr NL (1998). HARKing: Hypothesizing after the results are known. Pers Soc Psychol Rev 2: 196-217.
12. Kriegeskorte N, Simmons WK, Bellgowan PSF, Baker CI (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12: 535-540.
13. Marino MJ (2014). The use and misuse of statistical methodologies in pharmacology research. Biochem Pharmacol 87: 78-92.
14. Motulsky HJ (2014a) Intuitive biostatistics. 3rd ed. Oxford University Press, New York.
15. Motulsky HJ (2014b) Using simulations to calculate the false discovery rate. Available at http://www.graphpad.com/support/faqid/1923/ (accessed June 1, 2014).
16. Motulsky HJ (2014c). Opinion: never use the word 'significant' in a scientific paper. Pp. 25155 in Advances in regenerative biology vol. 1. Available at http://dx.doi.org/10.3402/arb.v1.25155 (accessed September 16, 2014).
17. Prinz F, Schlange T, Asadullah K (2011). Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov 10: 712-713. doi:10.1038/
18. Simmons J, Nelson L, Simonsohn U (2011). False-positive psychology: undisclosed flexibility in data collection and analysis allow presenting anything as significant. Psychol Sci 22: 1359-1366.
19. Ziliak S, McCloskey DN (2008). The cult of statistical significance: how the standard error costs us jobs, justice, and lives. University of Michigan Press, Ann Arbor.