Comparison of Two Samples

Part of the Springer Texts in Statistics book series (STS)


Problems of comparing two samples arise frequently in medicine, sociology, agriculture, engineering, and marketing. The data may have been generated by observation or may be the outcome of a controlled experiment. In the latter case, randomization plays a crucial role in gaining information about possible differences in the samples which may be due to a specific factor. Full nonrestricted randomization means, for example, that in a controlled clinical trial there is a constant chance of every patient getting a specific treatment. The idea of a blind, double blind, or even triple blind set{up of the experiment is that neither patient, nor clinician, nor statistician, know what treatment has been given. This should exclude possible biases in the response variable, which would be induced by such knowledge. It becomes clear that careful planning is indispensible to achieve valid results.

Another problem in the framework of a clinical trial may consist of the fact of a systematic effect on a subgroup of patients, e.g., males and females. If such a situation is to be expected, one should stratify the sample into homogeneous subgroups. Such a strategy proves to be useful in planned experiments as well as in observational studies.


Independent Group Binary Response Discordant Pair Pair Design Cleaning Technique 
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.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Institut für StatistikLudwig-Maximilians-UniversitätMünchenGermany
  2. 2.Department of Mathematics & StatisticsIndian Institute of TechnologyKanpurIndia

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