Skip to main content

Statistics in Experimental Stroke Research: From Sample Size Calculation to Data Description and Significance Testing

  • Protocol
  • First Online:
Rodent Models of Stroke

Part of the book series: Neuromethods ((NM,volume 120))

Abstract

Experimental stroke researchers take samples from populations (e.g., certain mouse strains) and make inferences about unknown parameters (e.g., infarct sizes, outcomes). They use statistics to describe their data, and they seek formal ways to decide whether their hypotheses are true (“Compound X is a neuroprotectant”). Unfortunately, experimental stroke research at present lacks statistical rigor in designing and analyzing its results, and this may have negative consequences for its predictiveness. This chapter aims at giving a general introduction into the do’s and don’ts of statistical analysis in experimental stroke research. In particular, we will discuss how to design an experimental series and calculate necessary sample sizes, how to describe data with graphics and numbers, and how to apply and interpret formal tests for statistical significance. A surprising conclusion may be that there are no formal ways of deciding whether a hypothesis is correct or not and that we should focus instead on biological (or clinical) significance as measured in the size of an effect and on the implications of this effect for the biological system or organism. “Good evidence” that a hypothesized effect is real comes from replication across multiple studies; it cannot be inferred from the result of a single statistical test!

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bederson JB, Pitts LH, Tsuji M, Nishimura MC, Davis RL, Bartkowski H (1986) Rat middle cerebral artery occlusion: evaluation of the model and development of a neurologic examination. Stroke 17:472–476

    Article  CAS  PubMed  Google Scholar 

  2. Buchan A, Pulsinelli WA (1990) Hypothermia but not the N-methyl-D-aspartate antagonist, MK-801, attenuates neuronal damage in gerbils subjected to transient global ischemia. J Neurosci 10:311–316

    Google Scholar 

  3. Hunt H. Boxplots in Excel. http://staff.unak.is/not/andy/StatisticsTFV0708/Resources/BoxplotsInExcel.pdf. 29 Dec 2015

  4. Harlow LL, Mulaik SA, Steiger JH (eds) (1997) What if there were no significance tests? Lawrence Erlbaum Associates, London

    Google Scholar 

  5. O’Hagan A, Luce R (2003) A primer on Bayesian statistics in health economics and outcomes research. https://www.shef.ac.uk/polopoly_fs/1.80635!/file/primer.pdf. 29 Dec 2015

  6. Sterne JAC, Smith GD (2001) Sifting the evidence – what’s wrong with significance tests? Br Med J 322:226–231

    Article  CAS  Google Scholar 

  7. Ziliak ST, McCloskey DN (2008) The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Univ of Michigan Press, Ann Arbor

    Google Scholar 

  8. Kirk RE (1996) Practical significance: a concept whose time has come. Edu Psychol Meas 56:746–759

    Google Scholar 

  9. Curran-Everett D, Benos DJ (2004) Guidelines for reporting statistics in journals published by the American Physiological Society. Am J Physiol 28(3):85–87

    Google Scholar 

  10. G*Power 3. http://www.gpower.hhu.de/. 29 Dec 2015

  11. Dirnagl U (2006) Bench to bedside: the quest for quality in experimental stroke research. J Cereb Blood Flow Metab 26:1465–1478

    Article  PubMed  Google Scholar 

  12. Schmidt FL, Hunter JE (1997) Eight common but false objections to the discontinuation of significance testing in the analysis of research data. In: Harlow L, Mulaik SA, Steiger JH (eds) What if there were no significance tests? Lawrence Erlbaum Associates, London, pp 37–64

    Google Scholar 

  13. Mulaik SA, Raju NS, Harshman RA (1997) There is a time and a place for significance testing. In: Harlow L, Mulaik SA, Steiger JH (eds) What if there were no significance tests? Lawrence Erlbaum Associates, London, pp 66–115

    Google Scholar 

  14. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafò MR (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365–376

    Article  CAS  PubMed  Google Scholar 

  15. Krzywinski M, Altman N (2013) Points of significance: power and sample size. Nat Methods 10:1139–1140

    Article  CAS  Google Scholar 

  16. Simonsohn U (2015) Small telescopes: detectability and the evaluation of replication results. Psychol Sci 26:559–569

    Article  PubMed  Google Scholar 

  17. Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2(8):e124

    Article  PubMed  PubMed Central  Google Scholar 

  18. Oakes M (1986) Statistical inference. Chichester: Wiley

    Google Scholar 

  19. Kimmelman J, Mogil JS, Dirnagl U (2014) Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol 12:e1001863

    Article  PubMed  PubMed Central  Google Scholar 

  20. Llovera G, Hofmann K, Roth S, Salas-Pérdomo A, Ferrer-Ferrer M, Perego C, Zanier ER, Mamrak U, Rex A, Party H, Agin V, Fauchon C, Orset C, Haelewyn B, De Simoni MG, Dirnagl U, Grittner U, Planas AM, Plesnila N, Vivien D, Liesz A (2015) Results of a preclinical randomized controlled multicenter trial (pRCT): Anti-CD49d treatment for acute brain ischemia. Sci Transl Med 7:299ra121

    Google Scholar 

  21. Motulsky HJ (2015) Common misconceptions about data analysis and statistics. J Pharmacol Exp Ther 351:200–205

    Article  Google Scholar 

  22. Festing MF, Nevalainen T (2014) The design and statistical analysis of animal experiments: introduction to this issue. ILAR J 55:379–382

    Article  CAS  PubMed  Google Scholar 

  23. Nuzzo R (2014) Scientific method: statistical errors. Nature 506(7487):150–152

    Article  CAS  PubMed  Google Scholar 

  24. Aban IB, George B (2015) Statistical considerations for preclinical studies. Exp Neurol 270:82–87

    Article  PubMed  PubMed Central  Google Scholar 

  25. Dirnagl U. To infinity and beyond. http://dirnagl.com. 29 Dec 2015

  26. Li X, Blizzard KK, Zeng Z, DeVries AC, Hurn PD, McCullough LD (2004) Chronic behavioral testing after focal ischemia in the mouse: functional recovery and the effects of gender. Exp Neurol 187:94–104

    Article  PubMed  Google Scholar 

  27. Plesnila N, Zinkel S, Le DA, Amin-Hanjani S, Wu Y, Qiu J, Chiarugi A, Thomas SS, Kohane DS, Korsmeyer SJ, Moskowitz MA (2001) BID mediates neuronal cell death after oxygen/glucose deprivation and focal cerebral ischemia. Proc Natl Acad Sci U S A 98:15318–15323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Emerson JD, Moses LE (1985) A note on the Wilcoxon-Mann-Whitney test for 2 x k ordered tables. Biometrics 41:303–309

    Article  CAS  PubMed  Google Scholar 

  29. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Mahwah, NJ

    Google Scholar 

  30. Simon S. Sample size for the Mann-Whitney U test. http://www.pmean.com/00/mann.html. 29 Dec 2015

  31. Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD (2015) The extent and consequences of p-hacking in science. PLoS Biol 13:e1002106

    Article  PubMed  PubMed Central  Google Scholar 

  32. Colquhoun D (2014) An investigation of the false discovery rate and the misinterpretation of p-values. R Soc Open Sci 1:140216

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ulrich Dirnagl .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this protocol

Cite this protocol

Dirnagl, U. (2016). Statistics in Experimental Stroke Research: From Sample Size Calculation to Data Description and Significance Testing. In: Dirnagl, U. (eds) Rodent Models of Stroke. Neuromethods, vol 120. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-5620-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-5620-3_19

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-5618-0

  • Online ISBN: 978-1-4939-5620-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics