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In the simplest sense, data dredging is described as the act of seeking information from a set of data than it actually contains. Procedures are discussed for basic experimental design, testing assumptions and outliers, assessing statistical power, analyzing less-than detection limit data, and interpreting statistical test results.Īpproved for public release distribution is unlimited.In contrast, the traditional or conventional scientific method of data dredging begins with the hypothesis and then extends through the stages of data examination.Īlternatively conducted for unethical purposes, data dredging is a data mining process that possibly circumvents the traditional techniques of data mining which may then results in premature conclusions. This report explores the impact of nonideal data on the performance of statistical tests recommended for dredged sediment evaluations. This in turn increases the likelihood of drawing false inferences concerning the potential of a dredged sediment for adverse biological effects. Such nonideal data can seriously affect the error rates of statistical tests. However, the resulting data are frequently problematic for standard statistical procedures because of improper experimental design, insufficient replication, failure to meet statistical test assumptions, outliers, and missing or below detection limit observations.
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Please use this identifier to cite or link to this item:Īpplications guide for statistical analyses in dredged sediment evaluationsĭredging Operations Technical Support Program (U.S.)Įngineer Research and Development Center (U.S.)Ībstract: Dredged sediment evaluations often require statistical analysis of chemical or biological test results.
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