Descriptive Statistics and Interval Estimation

Jean-Michel Josselin and Benoît Le Maux
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Jean-Michel Josselin: University of Rennes 1
Benoît Le Maux: University of Rennes 1

Chapter 3 in Statistical Tools for Program Evaluation, 2017, pp 45-88 from Springer

Abstract: Abstract This chapter reviews the different statistical methods used to describe a sample and make inference for a larger population. Despite its apparent simplicity, one should not underestimate the importance of the task, especially in the context of public policies. Providing basic descriptive statistics to point out the issues that must be addressed is a preliminary and necessary step in program evaluation (Sect. 3.1). One-way and two-way tables summarize the data in a very efficient manner (Sect. 3.2). Bar graphs, pie charts, histograms, line graphs and radar charts can also be generated at the evaluator’s convenience (Sect. 3.3). To go further, numerical analysis rests on measures of central tendency (mode, median, and mean), and of dispersion (interquartile range, variance, standard deviation, coefficient of variation) (Sect. 3.4). The asymmetry of a distribution and its “tailedness” can be approximated by the skewness and kurtosis coefficients (Sect. 3.5). Last, in most cases, the description of a database is done in the context of a sample survey. Any generalization to the population of interest thus involves the calculation of confidence intervals (Sect. 3.6). Several examples illustrate the methods.

Keywords: Descriptive statistics; Central tendency; Variability; Skewness; Kurtosis; Confidence interval (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-52827-4_3

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