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Uncovering Data for Decision Making With Critical Statistical Analysis
Abstract
Every field of study generates a huge amount of data. The volume of data generated leads to information overload, and the ability to make sense of all these data is becoming increasingly important. This requires a good understanding of the data to be analyzed and different statistical techniques to be used in that context. On the basis of the issues important to the data set as well as other practical considerations, it is necessary to select appropriate methods to apply to the problem under study. This work focuses on different issues arising in the context of data analysis which need attention like understanding classifications of data, magnitude of errors in measurement, missing observations in the data set, outlier observations and their influences on the conclusion derived from the data, non-normal data, meta analysis, etc. In the process of discussion some examples have been included to illustrate how critical a data analysis procedure could be in order to make a meaningful decision from a data set.
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