Permutation Tests and Resampling Techniques
Part of the Springer Series in Statistics book series (SBH)
- 167 Downloads
Recall that in significance testing we observe data and propose as a null hypothesis a statistical model that could have generated such data, lacking a treatment effect. We then calculate the probability of the observed data. If that probability is very low, we conclude that the null hypothesis is in adequate to describe how the data were generated. To calculate the probability, we consider a set of possible observations:
The data we observed plus all possible patterns of data that would have been “more extreme” than these data.
KeywordsPermutation Test Percent Coverage Randomization Pattern Percent Confidence Interval Empirical Distribution Function
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.
Unable to display preview. Download preview PDF.
© Springer-Verlag New York, Inc. 1992