Statistical Quality Control By M Mahajan Pdf.rar 41
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A statistical meta-analysis is a formal, systematic, and coherent method of combining multiple quantitative studies to generate a single, definitive conclusion. The goal of meta-analysis is to produce summary statistics that are more reliable than the data from each individual study. Meta-analysis is often used to detect publication bias, which describes the situation in which studies with positive results are more likely to get published than studies with negative results. This can lead to a distorted view of the information in the scientific literature and cause researchers to draw erroneous conclusions [5]. In a meta-analysis, each study is weighted by the inverse of the variance of the effect size observed in that study. This procedure ensures that studies with larger samples or longer follow-up times are weighted more heavily than studies with smaller samples or shorter follow-up time. Likewise, information from multiple studies can be combined to produce more precise estimates than would be available from a single study. Even small studies can contribute to a large pooled sample, as long as the studies are similar in quality and the data reported in the individual studies are similar in type. The results of a meta-analysis are often presented graphically as a forest plot, which shows the overall mean effect size of all studies for each intervention. The precision of the estimate is indicated by the size of the box (or its square root, when the box is asymmetrical), and the reliability of the estimate is indicated by the width of the bar, which reflects the extent of variation in the effect size among the studies [14].
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