An Introduction to Statistics for Librarians (Part Three): An Introduction to Statistical Tests
DOI:
https://doi.org/10.18060/27969Keywords:
parametric tests, non-parametric tests, p-values, statistical significanceAbstract
In the previous two columns, types of data, central tendency and distribution were discussed. This installment will build upon that content and explore various statistical tests. Three scenarios are introduced to illustrate how tests can be selected based on the type of data, the number of groups, and the distribution of the data. The column discusses interpreting test results, including challenges around establishing causation and the potential overreliance on p-values.
References
Bakker CJ. An Introduction to Statistics for Librarians (Part One): Types of Data. Hypothesis Res J Health Inf Prof. 2022;34(1). doi:10.18060/26428
Bakker CJ. An Introduction to Statistics for Librarians (Part Two): Frequency Distributions and Measures of Central Tendency. Hypothesis Res J Health Inf Prof. 2023;35(1). doi:10.18060/27162
Carlson S. Data analysis: Making sense of the numbers. Presented at: Department of Family Medicine & Community Health, University of Minnesota; 2016; Minneapolis, MN.
Hoskin T. Parametric and Nonparametric: Demystifying the Terms. Mayo Clinic. https://www.mayo.edu/research/documents/parametric-and-nonparametric-demystifying-the-terms/doc-20408960
Herzog MH, Francis G, Clarke A. Variations on the t-Test. In: Understanding Statistics and Experimental Design: How to Not Lie with Statistics. Springer International Publishing; 2019:51-59. Accessed May 28, 2022. http://library.oapen.org/handle/20.500.12657/23029
Sarty GE. Introduction to Applied Statistics for Psychology Students. University of Saskatchewan Open Press; 2022. Accessed December 30, 2023. https://openpress.usask.ca/introtoappliedstatsforpsych/
Jarman KH. Bunco, bricks, and marked cards: Chi-squared tests and how to beat a cheater. In: Beyond Basic Statistics: Tips, Tricks, and Techniques That Every Data Scientist Should Know. Wiley; 2015:47-68.
Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305-307. doi:10.1038/d41586-019-00857-9
Kennedy-Shaffer L. Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing. Am Stat. 2019;73(sup1):82-90. doi:10.1080/00031305.2018.1537891
Downloads
Published
How to Cite
License
Copyright (c) 2024 Caitlin Bakker
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All works in Hypothesis are licensed under a Creative Commons Attribution 4.0 International license. Authors own copyright of their articles appearing in Hypothesis. Readers may copy articles without permission of the copyright owner(s), as long as the author(s) and the Medical Library Association are acknowledged in the copy, and the copy is used for educational, not-for-profit purposes. For any other use of articles, please contact the copyright owner(s).
