Publisher Home ↗ editor@randspublications.org
International Journal of Social Sciences, Language and Linguistics eISSN 2051-686X Submit Article
Research Articles Open access

Navigating Statistical Inference: Addressing Enduring Misconceptions in Social Science Hypothesis Testing

Published
Pages 06-09
Views 107
Downloads 214

Authors

  • Dr. Eleanor Shaw Department of Sociology, University of Manchester, Manchester, United Kingdom Author
  • Prof. Jacob M. Lin Department of Political Science, University of California, Berkeley, United States Author
  • Dr. Priya Natarajan Centre for Quantitative Social Science, Indian Statistical Institute, Kolkata, India Author

DOI

https://doi.org/10.55640/ijssll-05-04-02 ↗

Abstract

Despite decades of statistical education, fundamental misconceptions about hypothesis testing persist in the social sciences. This article explores common errors in the interpretation and application of statistical inference—such as misunderstanding p-values, conflating statistical with practical significance, and over-reliance on null hypothesis significance testing (NHST). Drawing on recent methodological critiques and pedagogical studies, the paper analyzes how these misconceptions shape research outcomes, influence publication decisions, and perpetuate flawed scientific reasoning. The article further offers actionable recommendations for improving statistical literacy among researchers, including the adoption of alternative inferential approaches such as Bayesian inference, confidence intervals, and effect size reporting. By unpacking the roots and repercussions of these statistical misinterpretations, this study aims to foster a more nuanced and transparent approach to hypothesis testing in the social sciences.

Keywords

Statistical inference , hypothesis testing , p-values , social science research , statistical misconceptions , null hypothesis significance testing , research methodology

Downloads

Download data is not yet available.

References

1. Oakes, Michael W. Statistical Inference. Chestnut Hill: Epidemiology Resources, Inc., 1990. [Google Scholar]

2. McClave, James, and Terry Sincich. Statistics, 12th ed. Boston: Pearson, 2013. [Google Scholar]

3. Bakeman, Roger. Understanding Social Science Statistics: A Spreadsheet Approach. Hillsdale: Lawrence Erlbaum Associates, 1992. [Google Scholar]

4. Daly, Leslie, and Geoffrey J. Bourke. Interpretation and Uses of Medical Statistics, 5th ed. Oxford: Blackwell Science, 2000. [Google Scholar]

5. Hopkins, Kenneth D., and Gene V. Glass. Basic Statistics for the Behavioral Sciences. Englewood Cliffs: Prentice-Hall, 1978. [Google Scholar]

Welkowitz, Joan, Barry H. Cohen, and R. Brooke. New York: Wiley, 2011. [Google Scholar]

6. “Type I and Type II Errors.” Wikipedia, the Free Encyclopedia. Available online: http://en.wikipedia.org/wiki/Type_I_and_type_II_error.s (accessed on 22 April 2015).

7. Healey, Joseph F. Statistics: A Tool for Social Research, 9th ed. Stamford: Cengage Learning, 2011. [Google Scholar]

8. Bachman, Ronet, and Raymond Paternoster. Statistical Methods for Criminology and Criminal Justice. New York: Mcgraw-Hill, 1997. [Google Scholar]

9. Bhattacherjee, Anol. Social Science Research: Principles, Methods, and Practices. Tampa: Global Text Project, 2012, Available online: http://scholarcommons.usf.edu/oa_textbooks/3 (accessed on 7 May 2015).

10. Weisburd, David, and Chester Britt. Statistics in Criminal Justice, 3rd ed. New York: Springer Science & Business Media, LLC, 2007. [Google Scholar]

11. Anthony, Denis. Understanding Advanced Statistics: A Guide for Nurses and Health Care Researchers. Edinburgh: Churchill Livingstone, 1999. [Google Scholar]

12. Suchmacher, Mendel, and Mauro Geller. Practical Biostatistics: A User-Friendly Approach for Evidence-Based Medicine. Amsterdam: Academic, 2012. [Google Scholar]

Explore related research

Similar Articles

41-50 of 146
Continue exploring

Most read articles by the same author(s)

Search the journal

Find articles by title, author or keyword.