Linked Life Data

11778680

Source:http://linkedlifedata.com/resource/pubmed/id/11778680

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2002-1-7
pubmed:abstractText
A frequently used experimental design in psychological research randomly divides a set of available cases, a local population, between 2 treatments and then applies an independent-samples t test to either test a hypothesis about or estimate a confidence interval (CI) for the population mean difference in treatment response. C. S. Reichardt and H. F. Gollob (1999) established that the t test can be conservative for this design-yielding hypothesis test P values that are too large or CIs that are too wide for the relevant local population. This article develops a less conservative approach to local population inference, one based on the logic of B. Efron's (1979) nonparametric bootstrap. The resulting randomization bootstrap is then compared with an established approach to local population inference, that based on randomization or permutation tests. Finally, the importance of local population inference is established by reference to the distinction between statistical and scientific inference.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1082-989X
pubmed:author
pubmed:issnType
Print
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
402-12
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Random assignment of available cases: bootstrap standard errors and confidence intervals.
pubmed:affiliation
Department of Statistics, University of Washington, Seattle 98195-4322, USA. cliff@ms.washington.edu
pubmed:publicationType
Journal Article