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Statistical Inference Focusing on a Single Mean

In: Foundations of Applied Statistical Methods

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  • Hang Lee

    (Massachusetts General Hospital, Department of Biostatistics)

Abstract

Statistical inference is to infer whether or not the observed sample data are evidencing the population characteristics of interest. If the whole population data were gathered collectively then there is no room for uncertainty about the population due to a sampling and the statistical inference is unnecessary. It is ideal but unrealistic to collect the whole population data and complete the investigation solely by descriptive data analysis. For this reason, a smaller size of sample data set than that of the whole population is gathered for an investigation. Since the sample data set does not populate the entire population, it is not identical to the population. This chapter will discuss 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) forms and paradigms of drawing inference, and (4) good study design as a solution to minimize the unavoidable errors contained in the sampling.

Suggested Citation

  • Hang Lee, 2014. "Statistical Inference Focusing on a Single Mean," Springer Books, in: Foundations of Applied Statistical Methods, edition 127, chapter 0, pages 31-62, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-02402-8_2
    DOI: 10.1007/978-3-319-02402-8_2
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