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Using Effect Size—or Why the P Value Is Not Enough
Open accessAbstract
These statements about the importance of effect sizes were made by two of the most influential statistician-researchers of the past half-century. Yet many submissions to Journal of Graduate Medical Education omit mention of the effect size in quantitative studies while prominently displaying the P value. In this paper, we target readers with little or no statistical background in order to encourage you to improve your comprehension of the relevance of effect size for planning, analyzing, reporting, and understanding education research studies.In medical education research studies that compare different educational interventions, effect size is the magnitude of the difference between groups. The absolute effect size is the difference between the average, or mean, outcomes in two different intervention groups. For example, if an educational intervention resulted in the improvement of subjects' examination scores by an average total of 15 of 50 questions as compared to that of another intervention, the absolute effect size is 15 questions or 3 grade levels (30%) better on the examination. Absolute effect size does not take into account the variability in scores, in that not every subject achieved the average outcome.In another example, residents' self-assessed confidence in performing a procedure improved an average of 0.4 point on a Likert-type scale ranging from 1 to 5, after simulation training. While the absolute effect size in the first example appears clear, the effect size in the second example is less apparent. Is a 0.4 change a lot or trivial? Accounting for variability in the measured improvement may aid in interpreting the magnitude of the change in the second example.Thus, effect size can refer to the raw difference between group means, or absolute effect size, as well as standardized measures of effect, which are calculated to transform the effect to an easily understood scale. Absolute effect size is useful when the variables under study have intrinsic meaning (eg, number of hours of sleep). Calculated indices of effect size are useful when the measurements have no intrinsic meaning, such as numbers on a Likert scale; when studies have used different scales so no direct comparison is possible; or when effect size is examined in the context of variability in the population under study.Calculated effect sizes can also quantitatively compare results from different studies and thus are commonly used in meta-analyses.The effect size is the main finding of a quantitative study. While a P value can inform the reader whether an effect exists, the P value will not reveal the size of the effect. In reporting and interpreting studies, both the substantive significance (effect size) and statistical significance (P value) are essential results to be reported.For this reason, effect sizes should be reported in a paper's Abstract and Results sections. In fact, an estimate of the effect size is often needed before starting the research endeavor, in order to calculate the number of subjects likely to be required to avoid a Type II, or β, error, which is the probability of concluding there is no effect when one actually exists. In other words, you must determine what number of subjects in the study will be sufficient to ensure (to a particular degree of certainty) that the study has acceptable power to support the null hypothesis. That is, if no difference is found between the groups, then this is a true finding.Statistical significance is the probability that the observed difference between two groups is due to chance. If the P value is larger than the alpha level chosen (eg, .05), any observed difference is assumed to be explained by sampling variability. With a sufficiently large sample, a statistical test will almost always demonstrate a significant difference, unless there is no effect whatsoever, that is, when the effect size is exactly zero; yet very small differences, even if significant, are often meaningless. Thus, reporting only the significant P value for an analysis is not adequate for readers to fully understand the results.For example, if a sample size is 10 000, a significant P value is likely to be found even when the difference in outcomes between groups is negligible and may not justify an expensive or time-consuming intervention over another. The level of significance by itself does not predict effect size. Unlike significance tests, effect size is independent of sample size. Statistical significance, on the other hand, depends upon both sample size and effect size. For this reason, P values are considered to be confounded because of their dependence on sample size. Sometimes a statistically significant result means only that a huge sample size was used.3A commonly cited example of this problem is the Physicians Health Study of aspirin to prevent myocardial infarction (MI).4 In more than 22 000 subjects over an average of 5 years, aspirin was associated with a reduction in MI (although not in o
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“Concerning effect size, the r-value was 0.34.This would be counted as medium effect size (Sullivan & Feinn, 2012).”
From Teaching and Learning Science Using YouTube Videos and Discovery Learning in Primary School (2020) · cites this paper as Sullivan & Feinn, 2012
Experimental — accuracy not yet measuredIn the citing paper
“effect sizes, however, are independent of sample size (Sullivan and Feinn, 2012).”
From A chatbot-delivered intervention for optimizing social media use and reducing perceived isolation among rural-living LGBTQ+ youth: Development, acceptability, usability, satisfaction, and utility (2023) · cites this paper as Sullivan and Feinn, 2012
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Research works printed but not linked: 1
- Coe R. It's the effect size, stupid: what “effect size” is and why it is important. Paper presented at the 2002 Annual Conference of the British Educational Research Association, University of Exeter, Exeter, Devon, England, September 12–14, 2002. http://www.leeds.ac.uk/educol/documents/00002182.htm. Accessed March 23, 2012.
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“While effect sizes are increasingly reported to distinguish practical importance from mere statistical detection (Sullivan and Feinn, 2012), other tools address specific inferential gaps; for instance, VanderWeele’s E -value assesses the robustness of causal claims against unmeasured confounding…”
Oehme, S., Arteaga, J., Kayali, M. E., Wolf, F., Bartek, B., Gwinner, C., Winkler, T., Jung, T., & Milinkovic, D. (2026) · Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine · doi:10.1016/j.jisako.2026.101228
Eckel, N., Harzer, O., & Wieland, E. (2026) · Journal of Laboratory Medicine · doi:10.1515/labmed-2026-0084
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Fullerton, G. C., Starekova, J., Buelo, C., Harris, D. T., Souza, R. D. V., Faacks, A., Anagnostopoulos, A. A., Christofi, V. P., Murphy, A., Kadi, D., et al. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70575
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“Effect sizes were generally small [44], with no statistically significant between-genotype differences observed.”
Almond, L., Monaghan, P., McManus, M., Murphy, D., Halsall, L., & Tan, J. (2026) · Behavioral Science · doi:10.3390/bs16101811
“Therefore, Cramér’s V was calculated for all statistically significant associations to assess the magnitude of these differences consistent with established guidance on large-sample inference (Sullivan & Feinn, 2012; Lantz, 2013).”
Jiang, D., Tang, V. F. Y., Chow, E. O.-W., Kahlon, M., Yeung, D. Y.-L., Aubrey, R., Liao, J., Yu, J., Mo, P. K.-H., & Chou, K.-L. (2026) · International Psychogeriatrics · doi:10.1016/j.inpsyc.2026.100299
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Interpreting P values in clinical research: A practical guide for clinicians
Şener, A. (2026) · Turkish Journal of Emergency Medicine · doi:10.4103/tjem.tjem_350_26
“[4,6] Consider a study comparing a new thrombolytic protocol with standard treatment for reducing 30‑day mortality in patients with acute pulmonary embolism.”
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Salib, G., & Sharafi, Z. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3705249
“Whilethe majority of our findings are statistically signifi-cant, we echo prior research in cautioning against overreliance on significance testing alone [49].”
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“Additionally, meta-analysis can integrate findings across studies to highlight reproducible findings [42], which may differ from those associated with extremely low p -values within any single study [43,44].”
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“It has been recognized that an overpowered study with an excessively large sample size can detect trivial differences as statistically signi fi cant (42 – 45).”
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“…crisis, so validation must use stimuli representative of real commercial content, not extreme examples chosen to maximize a significant effect, and practical relevance should be judged by effect magnitude (Cohen’s d , partial eta-squared) rather than p values alone (Sullivan and Feinn, 2012).”
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“Consistent with our previous work, we used a low threshold of effect (SMD ≥ 0.2) 44 as important, given that distress is an iatrogenic harm.”
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“A measure of effect size, such as an odds ratio with a confidence interval for a 2 × 2 table, should also be reported when appropriate [8].”
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“…as a PET radionuclide using a ⁶⁸Ge/⁶⁸Ga generator system has expanded access to PET radiopharmaceutical production in cyclotron-free facilities (Sullivan & Feinn, 2012), facilitating the translation of research findings into clinical applications (Velikyan, 2015) Fibroblast Activation Protein…”
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Alotaibi, A. A., Habib, S. S., Gad, H. I., Doheyan, T. A., Aldosary, M., Aljasham, A., Alotaibi, A., Alshawakir, Y., Ahmad, F., & Al-Masri, A. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1847011
“Effect size was reported as partial eta squared ( η 2 p ), in line with recommended practice for reporting intervention magnitude (Sullivan and Feinn, 2012).”
Chun, J., Babakhani, K., Gonzalez, D. E., Dickerson, B. L., Sowinski, R. J., Rasmussen, C. J., & Kreider, R. B. (2026) · Journal of the International Society of Sports Nutrition · doi:10.1080/15502783.2026.2711032
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“Since effect sizes do not depend on sample size, they help indicate the potential significance of the intervention and describe the magnitude of observed effects independently of sample size influences [29, 30].”
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Geng, Y., van Dieën, J., & Bruijn, S. (2026) · bioRxiv · doi:10.1371/journal.pone.0355849
“For the one-sample t-test, effect size was quantified using Cohen’s d, with absolute values of 0.2, 0.5, and 0.8 considered as small, medium, and large effects, respectively [28].”
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“It is essential to note that statistical significance is not clinical significance [16].”
Wydooghe, E., Schutijser, P., Ngan, M. Y. O., Lee, M.-F., Lai, C., Wong, C.-L., Zhao, Z.-H., & Tang, F. M. K. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1846733
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“Following established conventions, we interpret d ≥ 0.8 as large effects, 0.5–0.79 as medium, 0.2–0.49 as small, and d < 0.2 as negligible [46].”
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“Effect size expresses the magnitude of an effect [7,8].”
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“…on its own to account for the attenuated estimate observed in that subgroup without invoking a genuine age-specific reversal, consistent with general guidance that effect-size and interval estimates, not p -values alone, should anchor the interpretation of null or borderline findings [29].”
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“39 Among the outcomes investigated, surface roughness represented the most robust fi nding because a statistically signi fi cant difference between materials was consistently observed throughout the study.”
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“Therefore, all eligible residents were consecutively included, and no formal a priori sample size calculation was performed, consistent with previous residency-based medical education studies (14, 15).”
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“Given the large sample size ( n = 669), even effects of small magnitude can achieve statistical significance, yet the proportion of variance explained by the model remains limited (60).”
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“A p -value indicates whether an effect exists, but it does not reveal the size of that effect, so the magnitude of the difference should be reported alongside the significance test (Sullivan and Feinn 2012).”
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“The effect sizes and 95% confidence intervals for the interaction between conditions (TEA and PLA) were calculated using Cohen’s d, with values 0.2 considered trivial, 0.2– 0.5 small, 0.5– 0.8 moderate, and ≥ 0.8 large [19].”
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“14,15,22 Another important methodological issue identified in this study was the inappropriate use of parametric statistical tests without verification of underlying assumptions.”
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“…coefficients are typically interpreted as follows: values ≥ 0.80 indicate a high level of correlation, 0.60 to 0.80 a strong level of correlation, 0.40 to 0.59 a moderate level of correlation, 0.20 to 0.39 a low level of correlation, and values 0.20 a very weak level of correlation 36 .”
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“The mean “impact” was Two-tailed, paired t-tests (for each of the 19 features) assessed significance of absolute differences (relative to GT features) caused by erosion vs. dilation (E × 1, D × 1), with effect size measured by Cohen’s d [22].”
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“In line with recent implementation science, reduced Adherence does not necessarily indicate poorer implementation quality, but may instead reflect purposeful contextualisation aimed at improving relevance and feasibility within specific school contexts [31, 32, 48].”
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“Overall, results suggest substantial overlap in the age distributions of the two samples and point to a difference that is statistically detectable but practically weak (Sullivan & Feinn, 2012).”
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“The corresponding Cohen’s d values were small to moderate, indicating that these baseline differences were clinically meaningful rather than merely statistically significant[35].”
Sangtam, T. Z. T., Savino, N., Hanah, S. S., Choudhary, J., Kumar, H., Vidyarthi, V., Rutsa, M. C., Devi, L. S., & Shivanagowda, G. P. (2026) · Comparative Clinical Pathology · doi:10.1007/s00580-026-03837-4
“… 0.05) indicates that an observed difference is unlikely to have arisen by chance, whereas biological significance refers to whether the magnitude of a difference is large enough to have a meaningful physiological or clinical consequence—and the two do not always coincide (Sullivan and Feinn 2012).”
InfluentialEsteban-Sepúlveda, S., Pascual-Garcia, M., Blanco, J., Sánchez-Ruano, R., Cervera, A., Moreno, M. A. C., Vidal-Thomàs, M., M. Company-Sancho, Feria-Raposo, I., Casbas, M. M., et al. (2026) · BMC Geriatrics · doi:10.1186/s12877-026-08027-z
“Given the longitudinal design, these estimates should be interpreted as descriptive indicators of change rather than as formal effect size measures for inferential comparisons [21].”
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Naghshtabrizi, B., Ataei, E., Naserian, S., Bagheri, L., & Ghobakhlou, M. (2026) · BMC Gastroenterology · doi:10.1186/s12876-026-05184-8
“This analysis confirmed 98.7% power to detect the observed large effect (Cohen’s w = 0.598; χ² test, two-tailed α = 0.05), substantially exceeding the conventional 80% threshold [21, 22].”
InfluentialPugni-Stanek, T., Merino-de-Miguel, S., Recuero, L., Magruga-Ramos, D., Litago, J., & Palacios-Orueta, A. (2026) · Remote Sensing · doi:10.3390/rs18152611
Sherman, A. D. F., Kebede, S., Akande, M., Duroseau, B., Pelkmans, J. L., Lishnoff, J., Allure, K., Poindexter, J., Schneider, J. S., Operario, D., et al. (2026) · Nursing Outlook · doi:10.1016/j.outlook.2026.102843
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“For the Wilcoxon Signed Rank Tests comparing the change scores between both periods, effect sizes (r) were calculated as r = z √ N and interpreted following Cohen’s d guidelines (small = 0.10, medium = 0.30, large = 0.50) [56–58].”
InfluentialAllan, A. C., Gamaldo, A. A., Thorpe, R. J., Roberts, J. D., Evans, M. K., & Zonderman, A. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23081055
“Ad-ditionally, following common effect size indices highlighted by Sullivan and Feinn [30], the magnitude of observed associations reflected a medium effect size for average reaction time on a cognitive test of attention and executive function/reasoning ( ∆ R 2 : 0.19).”
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“For RR, interpretation was based on the magnitude of relative risk reduction, in line with applied epidemiological practice and recommendations emphasizing practical significance beyond p -values [32].”
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“As such, we focus on reporting effect sizes in order to emphasise the magnitude of differences over statistical significance alone [17,18].”
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“…microbial ecosystem enriched in amino acid transformation and microbial protein synthesis, it may provide an ecological niche in which exogenous D-serine interacts with ruminal microorganisms and is associated with down-stream metabolic changes (Khan et al., 2011; Sullivan and Feinn, 2012).”
Indriani, N., Risnawita, Aniswita, & Rahmi, U. (2026) · YASIN · doi:10.58578/yasin.v6i5.11552
Jalene, S., Ciccotelli, J., Gatto, L., Ison, C., Carbajal, J., & Hooyman, A. (2026) · PLoS ONE · doi:10.1371/journal.pone.0353453
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Akbulut, S., Baran, A., Kızılay, E., Saritas, H., Sarıcı, K. B., Karabulut, E., & Ozer, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-61864-1
“Effect sizes were interpreted according to conventional thresholds (Cohen’s d: small = 0.20, medium = 0.50, large = 0.80; η 2 : small = 0.010, medium = 0.060, large = 0.140) 23,24 .”
InfluentialHu, D., Yi, J.-W., Du, Y.-Y., Liang, F., Tu, W., Wang, N., Qian, J.-L., Huang, S., Luo, P., & Xu, R. (2026) · Geo-Spatial Information Science · doi:10.1080/10095020.2026.2706342
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“Accordingly, the study is designed as a city-comparative diagnostic framework and does not make causal claims regarding behavior or health effects (1, 5, 6, 10, 12, 20–22).”
Influential
Metadata
- Authors
- Sullivan, G. M., & Feinn, R.
- Affiliations
- University of Connecticut (US)
- Corresponding author
- Sullivan, Gail M.
- Venue
- Journal of Graduate Medical Education
- Published
- 2012-09
- Volume
- 4
- Issue
- 3
- Pages
- 279-282
- ISSN
- 1949-8349 (ISSN-L)
- 1949-8357
- PMC
- PMC3444174
- OpenAlex
- W2107026277
- Cited by
- 5,553
- References
- 6
- Citations by year
- 2026: 607 · 2025: 877 · 2024: 759 · 2023: 547 · 2022: 574 · 2021: 499 · 2020: 416 · 2019: 419 · 2018: 320 · 2017: 222 · 2016: 215 · 2015: 83 · 2014: 9 · 2013: 4 · 2012: 2
- FWCI
- 35.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Graduate Medical Education (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Education › Educational Research and Methods
- Keywords
- effect size, p-values, statistical significance, substantive significance, standardized mean difference, medical education research, educational intervention, meta-analysis, simulation-based training, resident confidence, test scores, interpretation of study results, graduate medical education, reporting guidelines
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