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Statistical Inference Concentrating on a Single Mean

In: Foundations of Applied Statistical Methods

Author

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  • Hang Lee

    (Department of Medicine, Harvard Medical School, Massachusetts General Hospital Biostatistics Center)

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.

Suggested Citation

  • Hang Lee, 2023. "Statistical Inference Concentrating on a Single Mean," Springer Books, in: Foundations of Applied Statistical Methods, edition 2, chapter 0, pages 35-71, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-42296-6_2
    DOI: 10.1007/978-3-031-42296-6_2
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