Statistical Inference Focusing on a Single Mean

Hang Lee
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Hang Lee: Massachusetts General Hospital, Department of Biostatistics

Chapter Chapter 2 in Foundations of Applied Statistical Methods, 2014, pp 31-62 from Springer

Abstract: 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.

Keywords: Statistical Inference; Central Limit Theorem; Sample Standard Deviation; Observe Estimate; Data Evidence (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-02402-8_2

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DOI: 10.1007/978-3-319-02402-8_2

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