Advertisement

Hypothesis Tests

  • Thomas Haslwanter
Chapter
  • 16k Downloads
Part of the Statistics and Computing book series (SCO)

Abstract

This chapter describes a typical workflow in the analysis of statistical data. Special attention is paid to visual and quantitative tests of normality for the data. Then the concept of hypothesis tests is explained, as well as the different types of errors, and the interpretation of p-values is discussed. Finally, the common test concepts of sensitivity and specificity are introduced and explained.

Keywords

Null Hypothesis Positive Predictive Value Negative Predictive Value Negative Likelihood Ratio Positive Likelihood Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Altman, D. G. (1999). Practical statistics for medical research. New York: Chapman & Hall/CRC.Google Scholar
  2. Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2):486–489. doi:10.5812/ijem.3505. http://dx.doi.org/10.5812/ijem.3505 CrossRefGoogle Scholar
  3. Klamroth-Marganska, V., Blanco, J., Campen, K., Curt, A., Dietz, V., Ettlin, T., Felder, M., Fellinghauer, B., Guidali, M., Kollmar, A., Luft, A., Nef, T., Schuster-Amft, C., Stahel, W., & Riener, R. (2014). Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial. The Lancet Neurology, 13(2):159–166. doi:10.1016/S1474-4422(13)70305-3. http://dx.doi.org/10.1016/S1474-4422(13)70305-3 CrossRefGoogle Scholar
  4. Nuzzo, R. (2014). Scientific method: Statistical errors. Nature, 506(7487):150–152. doi:10.1038/506150a. http://www.nature.com/news/scientific-method-statistical-errors-1.14700 CrossRefGoogle Scholar
  5. Open Science Collaboration (OSC). (2015). Psychology. Estimating the reproducibility of psychological science. Science, 349(6251):aac4716. doi:10.1126/science.aac4716. http://dx.doi.org/10.1126/science.aac4716
  6. Sellke, T., Bayarri, M. J., & Berger, J. O. (2001). Calibration of p values for testing precise null hypotheses. The American Statistician, 55:62–71.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thomas Haslwanter
    • 1
  1. 1.School of Applied Health and Social SciencesUniversity of Applied Sciences Upper AustriaLinzAustria

Personalised recommendations