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Statistical Methods in Research

  • Domenico SpinaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 746)

Abstract

Statistical methods appropriate in research are described with examples. Topics covered include the choice of appropriate averages and measures of dispersion to summarize data sets, and the choice of tests of significance, including t-tests and a one- and a two-way ANOVA plus post-tests for normally distributed (Gaussian) data and their non-parametric equivalents. Techniques for transforming non-normally distributed data to more Gaussian distributions are discussed. Concepts of statistical power, errors and the use of these in determining the optimal size of experiments are considered. Statistical aspects of linear and non-linear regression are discussed, including tests for goodness-of-fit to the chosen model and methods for comparing fitted lines and curves.

Key words

Statistical analysis Descriptive and comparative statistics Parametric Non-parametric Normal distribution Statistical power 

References

  1. 1.
    Huff, D. (1955) How to lie with statistics Penguin Books, LondonGoogle Scholar
  2. 2.
    Ludbrook, J. (2008) Statistics in biomedical laboratory and clinical science: applications, issues and pitfalls. Med. Princ. Pract. 17, 1–13.PubMedCrossRefGoogle Scholar
  3. 3.
    Lew, M.J. (2008) On contemporaneous controls, unlikely outcomes, boxes and replacing the ‘Student’: good statistical practice in pharmacology, problem 3. Br. J. Phar-macol. 155, 797–803.PubMedCrossRefGoogle Scholar
  4. 4.
    Hancock, A.A., Bush, E.N., Stanisic, D., Kyncl, J.J., and Lin, C.T. (1988) Data normalization before statistical analysis: keeping the horse before the cart. Trends Pharmacol. Sci. 9, 29–32.PubMedCrossRefGoogle Scholar
  5. 5.
    Motulsky, H.J. (1995) Intuitive biostatistics. Oxford University Press, New YorkGoogle Scholar
  6. 6.
    Motulsky, H.J. (1999) Analysing data with Graphpad Prism. Graphpad Software Inc, San DiegoGoogle Scholar
  7. 7.
    Altman, D.G., Machin, D., Bryant, T.N., and Gardner, M.J. (2000) Statistics with confidence. BMJ Books, LondonGoogle Scholar
  8. 8.
    Sokhal, R.R. and Rolf, F.J. (1995) Biometry. W.H. Freeman & Co, New YorkGoogle Scholar
  9. 9.
    Petrie, A. and Sabin, C. (2000) Medical statistics at a glance. Blackwell Science, OxfordGoogle Scholar
  10. 10.
    Dyson, C. (2003) Choosing and using statistics. Blackwell Science, OxfordGoogle Scholar
  11. 11.
    Field, A. (2009) Discovering statistics using SPSS, 3rd ed., SAGE Publications Ltd, LondonGoogle Scholar
  12. 12.
    Klugh, H.E. (1986) Statistics: The essentials for research. 3rd ed., Lawrence Erlbaum Associates, LondonGoogle Scholar
  13. 13.
    Wallenstein, S., Zucker, C.L., and Fleiss, J.L. (1980) Some statistical methods useful in ­circulation research. Circ. Res. 47, 1–9.PubMedCrossRefGoogle Scholar
  14. 14.
    Quinn, G.P. and Kenough, M.J. (2002) Experimental design and data analysis for biologists. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  15. 15.
    Zar, J. (1999) Biostatistical analysis. 4th ed., Prentice Hall International, New JerseyGoogle Scholar
  16. 16.
    Matthews, J.N., Altman, D.G., Campbell, M.J., and Royston, P. (1990) Analysis of serial measurements in medical research. Brit. Med. J. 300, 230–235.PubMedCrossRefGoogle Scholar
  17. 17.
    Ludbrook, J. (1994) Repeated measurements and multiple comparisons in cardiovascular research. Cardiovasc. Res. 28, 303–311.PubMedCrossRefGoogle Scholar
  18. 18.
    Siegel, S. and Castellan, N.J. (1988) Nonpara­metric statistics for the behavioural sciences. 2nd ed., McGraw-Hill International, New YorkGoogle Scholar
  19. 19.
    Neave, H.R. and Worthington, P.L. (1988) Distribution-free tests. Unwin Hyman Ltd, LondonGoogle Scholar
  20. 20.
    Faul, F., Erdfelder, E., Lang, A.G., and Buchner, A. (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191.PubMedCrossRefGoogle Scholar
  21. 21.
    Festing, M.F. (2003) Principles: the need for better experimental design. Trends Pharmacol. Sci. 24, 341–345.PubMedCrossRefGoogle Scholar
  22. 22.
    Mead, R. (1988) The design of experiments. University Press, Cambridge, New YorkGoogle Scholar
  23. 23.
    Kenakin, T. (1996) Molecular pharmacology: a short course. Blackwell Science, OxfordGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.The Sackler Institute of Pulmonary Pharmacology, School of Biomedical ScienceKing’s College LondonLondonUK

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