‘Statistical significance’ in research: wider strategies to meaningfully interpret findings
evidence and practice    

‘Statistical significance’ in research: wider strategies to meaningfully interpret findings

Joan Lynch Lecturer, School of nursing and Midwifery, Western Sydney University, Campbelltown, New South Wales, Australia
Lucie M Ramjan Associate Professor, Western Sydney University, Campbelltown, New South Wales, Australia
Paul Glew Senior Lecturer, Western Sydney University, Campbelltown, New South Wales, Australia
Yenna Salamonson Professor, Western Sydney University, Campbelltown, New South Wales, Australia

Why you should read this article:
  • To understand the correct application of P-values in quantitative research

  • To review examples of incorrect uses of P-values and take note of their negative effect

  • To consider the magnitude of findings logically when reporting results, rather than relying on the P-value

Background The P-value is frequently used in research to determine the probability that the results of a study are chance findings. A value less than 0.05 was once typically considered only to mean that results are ‘statistically significant’, as it indicates the chance they are false positives is less than one in 20 (5%). However, P<0.05 has transcended into meaning a study has had positive findings and its results are true and meaningful, increasing the likelihood it will be published. This has led to researchers over-emphasising the importance of the P-value, which may lead to a wrong conclusion and unethical research practices.

Aim To explain what the P-value means and explore its role in determining results and conclusions in quantitative research.

Discussion Some researchers are calling for a move away from using statistical significance towards meaningful interpretation of findings. This would require all researchers to consider the magnitude of the effect of their findings, contemplate findings with less certainty, and place a greater emphasis on logic to support or refute findings – as well as to have the courage to consider findings from multiple perspectives.

Conclusion The authors argue that researchers should not abandon P-values but should move away from compartmentalising research findings into two mutually exclusive categories: ‘statistically significant’ and ‘statistically insignificant’. They also recommend that researchers consider the magnitudes of their results and report whether findings are meaningful, rather than simply focusing on P-values.

Implications for practice Lessening the importance of statistical significance will improve the accuracy of the reporting of results and see research disseminated based on its clinical importance rather than statistical significance. This will reduce the reporting of false positives and the overstatement of effects.

Nurse Researcher. doi: 10.7748/nr.2020.e1745

Peer review

This article has been subject to external double-blind peer review and has been checked for plagiarism using automated software

Correspondence

joan.lynch@westernsydney.edu.au

Conflict of interest

None declared

Lynch J, Ramjan L, Glew P et al (2020) ‘Statistical significance’ in research: wider strategies to meaningfully interpret findings. Nurse Researcher. doi: 10.7748/nr.2020.e1745

Published online: 22 October 2020

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