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{{New unreviewed article|source=ArticleWizard|date=November 2009}}
{{Expert-subject|Statistics|date=December 2009}}
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Conventional statistical methods do not provide exact solutions to many statistical problems, especially when the problem involves many nuisance parameters. As a result, practitioners often resort to approximate statistical methods or asymptotic statistical methods that are primarily based on large samples. With small samples, in most cases, these approximate methods and asymptotic methods perform very poorly. Due to the utilization of these approximate and asymptotic methods, experimenters often fail to detect the significance of their experiments. Furthermore, there are well-documented cases where these methods not only fail to detect the [[statistical significance]], but may also lead to misleading conclusions.
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