Using Effect Size-or Why the P Value Is Not Enough.

By Gail M Sullivan, R. Feinn. Journal of Graduate Medical Education, 2012

5,225 citations recorded by the source corpus.

Subfields: General Health Professions, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b251e0f1a1f2be049f89ce74400bb55b0ea603cd

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In General Health Professions

In Statistics and Probability

Citations in the corpus

Improving Methodological Standards in Behavioral Interventions for Cognitive Enhancement (2019, methodology): “One solution to these publication bias and p-hacking problems is to rely less on p values when reporting findings in publications (Barry et al. 2016; Sullivan and Feinn 2012).”
Gender differences in customer expectations and perceptions of corporate social responsibility (2016, background): “Substantive significance is concerned with meaning, as in, what do the findings say about population effects themselves?” Therefore, while substantive significance refers to the size of a relationship or an effect, statistical significance refers only to precision in measurement (Sullivan and Feinn,”
The need to report effect size estimates revisited. An overview of some recommended measures of effect size (2014, background): “Relying on the p-value alone for statistical inference does not permit an evaluation of the magnitude and importance of the obtained result [10, 12, 13].”
Improving Students’ Critical Thinking Skills in Cell-Metabolism Learning Using Stimulating Higher Order Thinking Skills Model (2019, background): “The result of this effect size is the impact of the treatment given to the variable to be developed (Sullivan & Feinn, 2012).”
Psychological factors associated with substance use initiation during the COVID-19 pandemic (2020, positive): “Although it is important to replicate and extend the findings from the current study with larger samples, previous research argues that effect size estimates are central to understanding how different groups are, and offers a comprehensive point of analysis (Sullivan and Feinn, 2012).”
Evaluation of a decided sample size in machine learning applications (2023, result): “Previous studies reported that a good effect size demonstrates a higher difference between two groups, whereas a small effect size had a trivial difference [27].”
The value of prior knowledge in machine learning of complex network systems (2016, positive): “For this reason, we opt to report effect size, which incorporates the magnitude of the difference in means (mean CA in our case) of the populations (Sullivan and Feinn, 2012).”
Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming (2023, neutral): “Finally, Cohen’s d is reported for effect size [78].”
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study (2023, result): “Furthermore, our results are consistent with Sullivan and Feinn (2012); Arora et al. (2022), which indicates that obtaining significant scores with alignment metrics can be challenging.”
Linking ethical leadership and ethical climate to employees' ethical behavior: the moderating role of person–organization fit (2020, background): “Sullivan and Feinn (2012) have posited that while the pvalue determines the existence of the effect, it does not reveal how big the effect is.”
If your P value looks too good to be true, it probably is: Communicating reproducibility and variability in cell biology (2019, background): “This paper is not an overview of statistical analyses; many have described the troubles with P values (Gardner and Altman, 1986; Sullivan and Feinn, 2012; Greenland et al., 2016) and there are several”