 Statistical Inference Concentrating on a Single MeanHang Lee
Chapter Chapter 2 in Foundations of Applied Statistical Methods, 2023, pp 35-71 from Springer Abstract: Abstract Statistical inference is to infer the population characteristics of interest through the observed sample data. If the whole collection of the population data were observed, then there would be no room for uncertainty about the true knowledge of the population, the statistical inference would be unnecessary, and the data analysis would be totally descriptive. For a real-world investigation, a smaller size of the sample data set than that of the whole population is gathered for inference. Since the sample data set does not populate the entire population and is not identical to the population, the inference using the sample data set becomes necessary. This chapter discusses the relationship between the population and sample by addressing (1) the uncertainty and errors in the sample, (2) underpinnings that are necessary for a sound understanding of the applied methods of statistical inference, (3) areas and paradigms of drawing inference, and (4) good study design to minimize/avoid inherent errors in the sample. Date: 2023 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-42296-6_2 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-42296-6_2 Access Statistics for this chapter More chapters in Springer Books from Springer |
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