Statistical Inference

Statistical Inference

DOI: 10.4018/978-1-68318-016-6.ch005
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Abstract

Statistical inference allows drawing conclusions from data. These analyses use a random sample of data taken from a population to describe and make inferences about the population. Inferential statistics are valuable when it is not convenient or possible to examine each member of an entire population. In this chapter, some concepts like ANOVA, Student's t-test, Chi-Square test, Mann-Whitney test and Kruskal-Wallis test will be presented. Given the insight of a particular phenomenon, after reading this chapter, the analyst will be able to, from that knowledge, infer possible new results.
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R Vs. Python

To make an inferential statistical analysis, the first step is checking the normality of the numerical variables. Depending on the normality or non-normality of the variables, parametric or non-parametric tests, respectively, should be used. In the following sections, we will present some suggestions to do inferential statistical analysis, in R and Python.

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