Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

Health Services Research & Development

Go to the ORD website
Go to the QUERI website

HSR&D Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

Hypotheses Testing as a Fuzzy Set Estimation Problem

Noorbaloochi S. Hypotheses Testing as a Fuzzy Set Estimation Problem. Communications in statistics: theory and methods. 2013 Apr 11; 42(10):1806-1820.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions



Abstract:

For many scientific experiments computing a p-value is the standard method for reporting the outcome. It is a simple way of summarizing the information in the data. One theoretical justification for p-values is the Neyman-Pearson theory of hypotheses testing. However, the decision making focus of this theory does not correspond well with the desire, in most scientific experiments, for a simple and easily interpretable summary of the data. Fuzzy set theory with its notion of a membership function gives a non-probabilistic way to talk about uncertainty. Here, we argue that for some situations, where a p-value is computed, it may make more sense to formulate the question as one of estimating a membership function of the subset of special parameter points which are of particular interest for the experiment. Choosing the appropriate membership function can be more difficult than specifying the null and alternative hypotheses but the resulting payoff is greater. This is because a membership function can better represent the shades of desirability among the parameter points than the sharp division of the parameter space into the null and alternative hypotheses. This approach yields an estimate which is easy to interpret and more flexible and informative than the cruder p-value.





Questions about the HSR&D website? Email the Web Team.

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.