Online Article

Introduction to parametric statistics

12th January 2023

Related topic: Quantitative research

Author: Ady Hameme N. A.

Parametric statistics are a set of statistical methods that rely on the assumption of a specific type of probability distribution for the data being analyzed. This assumption allows for the use of certain statistical tests and procedures, as well as the ability to make inferences about a population based on a sample.

One of the earliest works on parametric statistics is "Statistical Methods" by Snedecor and Cochran, published in 1967. In this book, the authors introduce the concept of a parametric test and provide guidelines for selecting the appropriate test based on the type of data being analyzed.

Zar's "Biostatistical Analysis" (1984) also discusses parametric statistics and expands upon the concept of a parametric test by introducing the idea of a non-parametric test as an alternative. Non-parametric tests do not assume a specific probability distribution for the data and are generally more robust, but they also have lower statistical power compared to parametric tests.

Hays' "Statistics" (1994) further develops the concept of parametric statistics by discussing the assumptions that underlie parametric tests and the consequences of violating these assumptions. The book also provides an overview of common parametric tests, such as t-tests and ANOVA, and how to interpret their results.

Levine and Berenson's "Essentials of Statistics for Business and Economics" (2009) also covers parametric statistics and provides practical examples of their use in business and economics. The book discusses the importance of understanding the assumptions of parametric tests and how to check for their validity.

Triola's "Elementary Statistics" (2012) provides a comprehensive introduction to parametric statistics, including the assumptions underlying parametric tests and the use of statistical software to perform these tests. The book also introduces the concept of hypothesis testing and provides examples of common parametric tests, such as t-tests and ANOVA.

Agresti and Finlay's "Statistical Methods for the Social Sciences" (2013) focuses on the use of parametric statistics in the social sciences. The book covers a range of parametric tests, including t-tests, ANOVA, and regression analysis, and provides examples of how these tests can be used to answer research questions in the social sciences.

McClave, Benson, and Sincich's "Statistics" (2015) provides a broad overview of parametric statistics and their applications. The book covers the assumptions underlying parametric tests, the use of statistical software to perform these tests, and the interpretation of their results. It also discusses the use of non-parametric tests as an alternative to parametric tests and the trade-offs between the two.

In conclusion, parametric statistics are a powerful tool for analyzing data and making inferences about a population based on a sample. A wide range of parametric tests are available, each with its own assumptions and applications. It is important to understand the assumptions underlying parametric tests and to choose the appropriate test based on the characteristics of the data being analyzed.

Cite this article: Ady Hameme, N. A. (2023, January 12). Introduction to parametric statistics. Retrieved <insert month> <insert date>, <insert year>, from https://www.myadvrc.com/publications/article-6

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