![]() There are two inherent problems on using multiple comparisons, first is the false positive result that is higher probability of detecting non-existing positive outcome, which is a procedural problem (type I error). Failure to use appropriate statistical methods weakens conclusions (Curran-Everett, 2000). Multiple comparisons means testing more than one hypothesis, in other words it is comparing two study groups for more than one output (outcome). Testing one hypothesis (effect of a drug A to control hypertension) is primary analysis, occasionally, researchers use data obtained from the study population to examine multiple outcome variables (secondary analysis). Testing the null hypothesis serves to guard against unjustifiable conclusions. Therefore, in data analysis, it is essential to identify the variables as input, output, or confounding (Campbell, 2006). Confounding (confusing) factors may shadow either cases, in the previous example, age or gender can be confounding factors. As an example testing the link between obesity and diabetes can be a cause effect relationship (obesity is a cause for diabetes) or the relationship between obesity (expressed by weight) and diabetes (expressed by blood glucose level). Alternatively, the purpose may be to test the null hypothesis (results are not because of chance), that is testing if the effect is only because of the input variables. The aim is to examine whether input (explanatory) variables relate to the effect (output or outcome) variables. Public health or medical research centers on an input to output relationship. The aim of this essay is to provide a brief yet, a comprehensive review on the problem of multiple comparisons, and how data fishing risks public health studies’ outcomes. If times need to be readjusted, notify the DQM Center so that the stored data can be backed out and corrected data can be manually entered.The problem of multiple comparisons is met with in many clinical trials, epidemiological studies, or public health studies, in which case, data fishing is a possibility. If data needs to be corrected, as long as the time stamps exactly match previously submitted data, it can be resent as if it were original data, as long as it is not over six weeks old. Contractors/system providers must contact the DQM Center to receive instructions to securely transmit the data. Scow data is collected on the scow and transmitted via email to the DQM Center using SOAP protocols. DQM QA Check Description – All Plant Types.Scow Certifications \ Data Quality Checks Scow Ullage Specifications – Civil Works.Scow Monitoring Specifications – Regulatory Scow Monitoring Specifications – Civil Works. ![]() ![]() Ullage – additionally requires the collection of bin ullage and volume measurements which can be used to validate draft data and estimate quantities In addition to location tracking, the profiles include: Monitoring – requires collection of draft and displacement values to assist in identifying when loading/unloading is occurring and potential leakage. DQM recognizes two levels of instrumentation, referred to as profiles. They can be mechanically or hydraulically offloaded or can self dispose material they can include flat deck or sealed barges as well as split hull or bottom dumping scows. DQM considers any floating plant which transports dredge material to be a scow.
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