Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Toutenburg, H., Shalabh (2009). Comparison of Two Samples. In: Statistical Analysis of Designed Experiments, Third Edition. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1148-3_2
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1148-3_2
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-1147-6
Online ISBN: 978-1-4419-1148-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)