'Statistical significance' in research: wider strategies to meaningfully interpret findings.

Abstract

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