The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An effect size primer: A guide for clinicians and researchers.
Ferguson, C. J. (2009) · Professional Psychology: Research and Practice · doi:10.1037/a0015808 · Cited by 3,467
Things I have learned (so far).
Cohen, J. (1990) · American Psychologist · doi:10.1037/0003-066x.45.12.1304 · Cited by 1,781
Beyond significance testing: Reforming data analysis methods in behavioral research.
Kline, R. B. (2004) · doi:10.1037/10693-000 · Cited by 1,034
Meta-Analysis of Multiple Primary Prevention Trials of Cardiovascular Events Using Aspirin
Bartolucci, A. A., Tendera, M., & Howard, G. (2011) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2011.02.325 · Cited by 185
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.
Cited by
Sorted by citations
Items 701–800 of 1,078
Childress, A. C., Brams, M. N., Cattaneo, M. J., & Silva, R. (2026) · Journal of Child and Adolescent Psychopharmacology · doi:10.1177/10445463261469764
“…endpoint across weekly assessments was performed to estimate the magnitude of treatment effects — an approach that is independent of sample size and commonly used to evaluate the clinical impact of ADHD medications (Cohen, 1988; Faraone, 2012; Faraone and Glatt, 2010; Sullivan and Feinn, 2012).”
Response feature analyses for repeated measures behavioral data
Burns, D., & Gerow, K. (2026) · Biology Open · doi:10.1242/bio.062488
“These results also yield a very large effect size (Cohen ’ s d =1.31) (Sullivan and Feinn, 2012).”
InfluentialRoccetti, M. (2026) · Frontiers in Aging · doi:10.3389/fragi.2026.1888676
Wang, Q., Liao, M., Xing, S., & Liao, Y. (2026) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-026-10230-3
“Effect sizes, unlike P values, are independent of sample size and convey the magnitude of an association rather than merely its statistical detectability [31].”
Costa, E. F. S., Andrade-Tubino, M. F., & Vianna, M. (2026) · Estuaries and Coasts · doi:10.1007/s12237-026-01776-5
“The pooled standard deviation was calculated as: where s p is the pooled standard deviation, s 1 and s 2 are the standard deviations of the two season-specific null dis - tributions, and n 1 and n 2 are the corresponding sample sizes (Lakens, 2013; Sullivan & Feinn, 2012 ).”
InfluentialHernández-Bernal, F., Subirós-Martínez, N., Gutiérrez-Ronquillo, J. H., Colina-Ávila, E., Guevara-Rodríguez, M., Estenoz-García, D., Catasús-Álvarez, K. M., Martín-Bauta, Y., Pérez-Saad, H., Manso-López, A. M., et al. (2026) · Journal of clinical neuroscience · doi:10.1016/j.jocn.2026.112195
Čandek-Potokar, M., Škrlep, M., Poklukar, K., Schlegel, P., & Bee, G. (2026) · Journal of animal physiology and animal nutrition · doi:10.1111/jpn.70090
“In addition to assessing the likelihood that treatment effect is not due to chance, effect size helps the reader understand the magnitude of the observed differences; both are essential for interpreting the importance of the treatment (Sullivan and Feinn 2012).”
Ajayi, A. A., Zhao, C., Schachter, J., Wade, B., & Bonilla, G. G. S. (2026) · Journal of American College Health · doi:10.1080/07448481.2026.2692541
“79,80 Model parameters were estimated using restricted maximum likelihood (REML), which is robust to sample sizes and unbalanced data.”
Almereb, O. T., Khaw, K., Alghababsheh, M., Karia, N., & Mosalam, A. J. (2026) · Management & Sustainability: An Arab Review · doi:10.1108/msar-07-2025-0239
Jia, B., Mohamad, M., Kamsin, I. F., & Shim, J. (2026) · Arab World English Journal · doi:10.24093/awej/call12.17
Hagner-Derengowska, M., Kacprzak, B., Taradaj, J., & Trybulski, R. (2026) · Scientific Reports · doi:10.1038/s41598-026-60885-0
“Reporting both raw-scale effects and standardized estimates can improve www.nature.com/scientificreports transparency, but standardized estimates require interpretation in relation to the denominator used for standardization and the clinical meaning of the original scale 49,50 .”
Shah, V., Constantin, K. L., Taddio, A., McMurtry, C., Lang, E., MacDonald, N. E., Logeman, C., & Uleryk, E. (2026) · The Clinical Journal of Pain · doi:10.1097/ajp.0000000000001381
Rana, E. (2026) · Journal of Rehabilitation Medicine · doi:10.2340/jrm.v58.46243
“Statistical significance alone may not always translate into meaningful functional improvement in rehabilitation settings (8).”
Upper-Limb asymmetry in wheelchair para-athletes: a comparative analysis across sports disciplines
Kokaly, M., Carvallo, A., Guzmán, C., Prieto, M. J. G., Henríquez, M., & Valencia, O. (2026) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2026.1858941
“Furthermore, Cohen’s d was considered to inform the effect size between the two groups (0.20 = small, 0.50 = medium, 0.80 = large, 1.30 = very large) (17).”
InfluentialNoviyanti, M., Sudirman, S., Kandaga, T., Nurhayati, S., & Isnawan, M. G. (2026) · European Journal of STEM Education · doi:10.20897/ejsteme/18921
Tezval, R., Haghighi, T. H., & Mirpour, Z. (2026) · Journal of Health Reports and Technology · doi:10.5812/jhrt-172209
“Schema therapy, particularly when conceptualized through the mode model, integrates cognitive, experiential, behavioral, and interpersonal techniques to address emotional symptoms and maladaptive coping behaviors (14).”
Nopitasari, B. L., Hadi, M. S., & Hijriani, N. (2026) · Lumbung Farmasi: Jurnal Ilmu Kefarmasian · doi:10.31764/lf.v7i2.35512
“Pertama, jumlah sampel relatif kecil sehingga daya uji (statistical power) menjadi rendah, meningkatkan risiko type II error atau kegagalan mendeteksi perbedaan yang sebenarnya ada (Sullivan & Feinn, 2012).”
Zhang, M., Zhang, C., Li, S., Li, T., & Han, Y. (2026) · PLoS ONE · doi:10.1371/journal.pone.0342176
“For example, Deng [7] observed in their study on tourism motivation and tourist loyalty that the effect size of tourism motivation on tourist satisfaction was merely 0.440 (medium)—considerably smaller than the 0.997 (large, Cohen’s d 0.2 small, 0.5 medium, 0.8 large [103]) reported here.”
InfluentialBertram, C. A., Ammeling, J., Bartel, A., Beamer, G., & Aubreville, M. (2026) · Veterinary Pathology-Supplement · doi:10.1177/03009858261461760
Alcázar-Peral, J. M., de la Parra, J. A. Á., Blanco, D., Blanco, Á., Barrios, M. E., Cristóbal, I., & Caramés, C. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1874919
“When analyzing professional experience, differences between rounds were assessed using Welch’s t -test, and standardized effect sizes were quantified using Hedges’ g (22).”
Intangible Resource Capital and Sustainable Value Creation
Ishak, R., Muhammad-Jamil, C., Che-Adam, N., & Abd-Mutalib, H. (2026) · PaperASIA · doi:10.59953/paperasia.v42i3b.1020
“As stated by Sullivan and Feinn (2012), it is also important to report both the effect size (substantive significance) and the p-value (statistical significance) beyond the p-value.”
Ferlito, J. V., Rolnick, N. D., Kamiş, O., de Queirós, V. S., Lopez, P., & Hughes, L. (2026) · British Journal of Sports Medicine · doi:10.1136/bjsports-2025-111452
“Effect sizes for SMD analyses were interpreted following Cohen 34 : small (≥0.2 to 0.49), moderate (≥0.5 to 0.79) and large (≥0.8).”
InfluentialKnowledge Level of Cardiovascular Disease Risk Factors and Sleep Quality in People With Epilepsy
Erdoğan, H., & Yakar, H. K. (2026) · Brain and Behavior · doi:10.1002/brb3.71573
“Cohen’s value is interpreted as: “0.2: small; 0.5: medium; 0.8: large,” and r value “ < 0.2: weak correlation, < 0.5: moderate correlation, > 0.8: strong correlation” (Sullivan and Feinn 2012 ).”
InfluentialClinical neuropsychological evaluation of patients referred from a post-COVID subspeciality clinic
Theeler, J., Comellas, A. P., Gehl, C. R., Garvin, L. A., Garg, A., Fain, S. B., & Hoth, K. F. (2025) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2536700
“Regarding the interpretation of effect sizes for the one sample t -tests, Cohen’s d > 0.2 and 0.5 were considered to be small, 0.5–0.8 were considered medium, and above 0.8 were considered large (Table 2) (Sullivan & Feinn, 2012).”
InfluentialAkbulut, S., Ozdemir, B., Akturk, U., & Sarıcı, K. B. (2026) · Medicina · doi:10.3390/medicina62071391
“…of group differences, including Cohen’s d for continuous variables compared between two groups (small = 0.2, medium = 0.5, large = 0.8) [36,37], eta-squared ( η 2 ) for ANOVA analyses (small = 0.01, medium = 0.06, large = 0.14) [36], and Cramér’s V for categorical variables (small = 0.1,…”
InfluentialCan, D. V., Nguyen, H. T. V., Duong, M. T. T., & Thai, H. T. (2026) · International Conference on Advanced Learning Technologies · doi:10.1109/icalt68022.2026.00063
“Effect sizes were also calculated to evaluate the practical significance of observed changes, with thresholds of 0.2, 0.5, 0.8, and 1.3 indicating small, medium, large, and very large effects, respectively [17].”
InfluentialShrestha, S., Shan, Y., Emerson, R., & Manning-Ouellette, A. (2026) · Journal of Civil Engineering Education · doi:10.1061/jceecd.eieng-2269
Shen, R.-F., Zhang, J.-J., Shen, F., Chen, Y., & Zhou, F. (2026) · Mechanical systems and signal processing · doi:10.1016/j.ymssp.2026.114446
Castro-Neto, H., Craveiro, C., Rêgo, M. D. D., Oliveira, P., Soares, R., & Peixoto, S. (2026) · Fisheries Management and Ecology · doi:10.1111/fme.70092
Manzoor, Z., Ehsan, M., Naseer, Z., Abdelrahman, K., Shafi, A., Raza, D., Raza, M., & Fnais, M. (2026) · Energy Strategy Reviews · doi:10.1016/j.esr.2026.102311
Temporal Patterns of Advanced Shot-Quality Metrics in Elite Men’s and Women’s European Football
de-la-Cruz-Torres, B., Ruiz-de-Alarcón-Quintero, A., & Navarro-Castro, M. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10070214
The role of particulate matter in surgical site infection pathogenesis: a scoping review
Kewalramani, D., Benton, J., Loftus, T. J., Ali, H., Harikrishnan, S., Randhawa, D., Adeyemi, O., Chattopadhyay, K., Brisbon, J., Madani, A., et al. (2026) · Journal of Hospital Infection · doi:10.1016/j.jhin.2026.06.027
Lafram, A., Mouiret, O., Naggar, Y. A., & Roky, R. (2026) · Toxicology Letters · doi:10.1016/j.toxlet.2026.113164
Pina, W. A. R. D. A., Lourenço, V. L. D. S., dos Anjos, V. R., Vivan, L., de Lira, C. A. B., Weiss, K., Knechtle, B., & Andrade, M. (2026) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2026.104620
Tinelli, C., Scotti, C., Casaccio, F., Zlatic, M., Baroni, G., Morelli, L., & Paganelli, C. (2026) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2026.109551
Samancı, M., Kaya, P., Aksoy, A., & Tunçer, G. (2026) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2026.07.015
Kulkarni, S., Hiranaka, T., & Parker, D. A. (2026) · Journal of ISAKOS Joint Disorders & Orthopaedic Sports Medicine · doi:10.1016/j.jisako.2026.101186
Jafarzadeh, S., Aval, M. H., & Jamali, J. (2026) · BMJ Paediatrics Open · doi:10.1136/bmjpo-2025-004203
A Safety Filter Approach for Closed-Loop Anesthesia Using the SQI-Model-Enhanced Digital Twin
Ayvaz, B., Yumuk, E., Copot, D., & Ionescu, C. (2026) · European Journal of Control · doi:10.1016/j.ejcon.2026.101578
Shih, Y.-L., Lin, C., Chen, W., Luo, S., Wang, Y., Li, S., Hwang, C., Hsieh, H.-C., & Wu, C. (2026) · Psycho-Oncology · doi:10.1002/pon.70552
Binasdevi, M., Abdurahman, A., Ramadhan, G. P., Mustafid, R. H., & Maulana, F. A. (2026) · Indonesian Journal of Islamic Education Studies (IJIES) · doi:10.33367/ijies.v9i1.8497 · Cited by 1
“As Sullivan and Feinn (2012) emphasized, p-values are heavily constrained by sample size, whereas effect sizes offer a more stable indication of an intervention's practical impact.”
Gouda, A., Arshad, M. Z., & Andrysek, J. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351930
Khalifa, G., Marzouk, A. M., Elnagar, A. K., Elkhwesky, Z., Nasir, N. S. M., Herzallah, A., & Elshaer, A. M. (2026) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-12-2025-1465
Aucancela, M., González-Briones, A., & Chamoso, P. (2026) · Electronics · doi:10.3390/electronics15132801
A new in silico model to precisely design focused ultrasound brain therapies
Conti, A., Grossi, M., de Andrade, P. C., Duggento, A., & Toschi, N. (2026) · Medical Physics (Lancaster) · doi:10.1002/mp.70520
Logie, C. H., Admassu, Z., Hasham, A., Kagunda, J., Evelia, H., Leggett, R., Gachoki, C., Mwangi, M., Gittings, L., Dorea, C. C., et al. (2026) · International Health · doi:10.1093/inthealth/ihag064
“41 , 42 Effec t siz es (ORs f or dichotomous outcomes, Cohen’s d for continuous outcomes 43 ) and confidence intervals are reported alongside P values to support interpretation of the magnitude and precision of observed associations, and borderline significant results are interpreted cautiously.”
Le, V., Christalle, E., Rutenkröger, M., Zill, J., Scholl, I., & Lindig, A. (2026) · Reproductive Health · doi:10.1186/s12978-026-02389-8
“We calculated effect size Cohen’s d for the t-tests and categorized them using the following thresholds: small (0.2), medium (0.5), large (0.8), and very large (1.3) [36].”
InfluentialHarju, A., Karttunen, E., Immonen, M., & Hallikas, J. (2026) · Supply Chain Management · doi:10.1108/scm-03-2026-0273
Zheng, Z., Guan, L., Gao, M., Tian, H., Ye, L., Wei, X., Chen, X., Ademuwagun, T., Zhang, K., Tang, D., et al. (2026) · European Journal of Pediatrics · doi:10.1007/s00431-026-07171-3
“Effect sizes were categorized as small (0.2 ≤ d 0.5), medium (0.5 ≤ d 0.8), or large ( d ≥ 0.8) based on Cohen’s benchmarks [28].”
InfluentialLi, M.-L., Zhang, J.-W., Su, X.-Q., Yu, X., Tong, J., Tian, X.-H., Dong, G., & Liu, Z. (2026) · Frontiers in Surgery · doi:10.3389/fsurg.2026.1853742
Tullah, N. H. (2026) · MALANG JOURNAL OF MIDWIFERY (MAJORY) · doi:10.31290/majory.v8i1.5956
Janković, D., Kukić, F., Radić, I., Čvorović, A., Dawes, J., & Dopsaj, M. (2026) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000005562
Noor, T., Mashkoor, M., Mubarak, N., & Haider, S. A. (2026) · Management Research Review · doi:10.1108/mrr-09-2025-0693
de Vasconcellos, C. F. C., Oliveira, T. A. S. D., Pellissari, M. C., Boavista, R. R. C., Sallum, J. M. F., & Sallum, J. M. F. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107236
Abdlekrim, N. B., Nabli, M. A., Dhahbi, W., Ceylan, H. I., Ștefănică, V., Romdhani, M., Dergaa, I., & Chamari, K. (2026) · PeerJ · doi:10.7717/peerj.21218
“Effect sizes for pairwise comparisons were calculated using Cohen’s d, interpreted as: trivial: <0.2; small: 0.2 ∼ < 0.5; moderate: 0.5 ∼≤ 0.8; large: >0.8 ( Sullivan & Feinn, 2012 ).”
InfluentialMotor imagery abilities in individuals who experience aphantasia
Wright, D. J., Scott, M. W., Esselaar, M., Braithwaite, E., & Holmes, P. S. (2026) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2026.109530 · Cited by 1
Belhadfa, E., Bowles, N., Shirley, K., Simon, A. A., Hamilton, V., & Kaplan, H. (2026) · doi:10.48550/arxiv.2606.12642
Personalised Perception: The Effect of Personality on the Sensory Effects of Psychedelics
Boardman, A. D., Whelan, J., & Ward, R. D. (2026) · Drug and Alcohol Review · doi:10.1111/dar.70172
Tahmasian, M., Bi, H., Åkerstedt, T., Bülow, R., Deantoni, M., Drzezga, A. E., Elman, J., Elmenhorst, D., Elmenhorst, E., Ewert, R., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9914920/v1
“Although these studies reported statistically significant group-level associations, the corresponding effect sizes were consistently very small (r range 0.0007 - 0.0057) (Keyes & Westreich, 2019; Lin et al., 2013; Spence et al., 2026; Sullivan & Feinn, 2012).”
InfluentialVillaverde, C., Wong, T., Shimelash, N., McCall, N., & Ripp, K. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09624-7
“Cohen’s d was calculated to determine the effect size, defined as small (0.2), medium (0.5), large (0.8), or very large (1.3) [21].”
InfluentialImpact of an Influenza and Pneumococcal Vaccination Course on Enhancing Pharmacists’ Knowledge
Waszkiewicz, M., Świtalski, J., Dąbrowski, P., Niedzielski, A., & Augustynowicz, A. (2026) · Medical Science Monitor · doi:10.12659/msm.953236
“The following thresholds were used to interpret effect size: small (d = 0.2), medium (d = 0.5), large (d = 0.8), and very large (d = 1.3) [19].”
InfluentialHu, L.-H., Cao, D.-H., Yuan, X., Wang, X., & Yuan, H.-F. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1827918
Sastre-Munar, A., Delextrat, A., & Romero-Franco, N. (2026) · Clinical Journal of Sports Medicine · doi:10.1097/jsm.0000000000001475
The Impacts of Southwest Indian Ocean Tropical Cyclones on Transient Weather in Réunion
Marinaki, A. M., & Fitchett, J. (2026) · International Journal of Climatology · doi:10.1002/joc.70456
“…difference between two group means in standard deviation units, with thresholds of small ( d = 0.2), medium ( d = 0.5), and large ( d ≥ 0.8) following the widely adopted benchmarks established by Cohen (1988) and subsequently validated across scientific disciplines (Sullivan and Feinn 2012).”
InfluentialFujs, D., Vavpotič, D., & Poženel, M. (2026) · Automated Software Engineering · doi:10.1007/s10515-026-00633-w
“We discuss each research question in turn and high-light how the results inform both educational practice and future research directions.”
Merifield, L. E., Toyama, M., Gooman, A., Teranishi-Hashimoto, C., Lim, E., Yamada, P. M., & Fukui, J. A. (2026) · Healthcare · doi:10.3390/healthcare14111556
“The effect size is considered small if its absolute value is less than 0.2 and large if it is greater than 0.8 [32].”
InfluentialPramudyo, V. H. F., Sedjati, S., & Yudiati, E. (2026) · Thalassas: An International Journal of Marine Sciences · doi:10.1007/s41208-026-01166-5
“Cohen’s d effect size small 0.2; medium 0.5; large 0.8; and very large 1.3 (Sullivan and Feinn 2012).”
InfluentialCognitive Sensory Motor Training Therapy in Stroke Patients: A Systematic Review
Ochandorena-Acha, M., Rierola-Fochs, S., Merchán-Baeza, J., Gónzalez-Sánchez, M., & Pérez-Cruzado, D. (2026) · Revista de neurología (Ed. impresa) · doi:10.31083/rn50584
“For interpretation, the Cohen definition of effect size was used (d = 0.2 small, d = 0.5 medium, d = 0.8 large) [25].”
InfluentialArianezhad, S. M., Safi, Y., Ahsaie, M. G., Mortazavi, H., & Aryanezhad, S.-S. (2026) · Scientific Reports · doi:10.1038/s41598-026-55394-z
“Effect sizes were interpreted as small (0.2), moderate (0.5), and large (≥ 0.8) 39,40 .”
InfluentialBai, R. (2026) · Journal of anthropology and archaeology · doi:10.1016/j.jaa.2026.101768
Svedell, L. A., Lidström-Holmqvist, K., Msghina, M., & Lindvall, M. A. (2026) · Complementary Therapies in Medicine · doi:10.1016/j.ctim.2026.103372
Vo, B., & Quy, H. T. K. (2026) · Research in Transportation Business & Management · doi:10.1016/j.rtbm.2026.101697
Haverila, K., Haverila, M., & McLaughlin, C. (2026) · Journal of Contemporary Marketing Science · doi:10.1108/jcmars-08-2025-0040
Abdalla, R., Alkhateeb, M., & Ayyash, M. (2026) · Int. J. Inf. Manag. Data Insights · doi:10.1016/j.jjimei.2026.100424
Choi, T.-H., Kim, S.-Y., & Lee, N. (2026) · Applied Sciences · doi:10.3390/app16115497
Arthroscopic rotator cuff repair: the effect of cuff tear size on outcomes and recovery trajectories
Tan, Y. C. J., Chiu, S.-T., & Tan, A. (2026) · JSES Reviews, Reports, and Techniques · doi:10.1016/j.xrrt.2026.100791
CsmR controls both, motility and cell shape, in Haloferax volcanii
Nußbaum, P., Grünberger, F., Neuschütz, F., Chou, K., Sivabalasarma, S., Eulitz, A., Sailer, A.-L., Vogl, K., Exterkate, M., He, W., et al. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012198
“Under such conditions, p-values alone are insufficient to assess the biological relevance of observed differences [67,68].”
Don't Skip A Beat: Deliberate Practice and Mastery Learning of Pediatric Electrocardiograms.
Boutis, K., Pusic, M., Etoom, Y., Patel, S., Johri, R., Pecaric, R., & Stephenson, E. (2026) · Jornal de Pediatria · doi:10.1016/j.jpeds.2026.115192
Graf, A., Kazantsev, A., & Bunik, V. (2026) · Biochemistry (Moscow) · doi:10.1134/s0006297926600444
“No correction for multiple comparisons was applied, as the primary interpretation relied on correlation strength and because appropriate correction is nontrivial in heterogeneous correlation structures [35].”
Sangalli, L., Mirfarsi, S., Mimms, C., & Prodoehl, J. (2026) · Oral surgery, oral medicine, oral pathology and oral radiology · doi:10.1016/j.oooo.2026.06.013
Diagnosing stagnation in problem framings and solution ideas during design thinking projects
Ito, A., Taoka, Y., Nakatani, M., & Saito, S. (2026) · Design Science · doi:10.1017/dsj.2026.10062
“Effect size was reported as an indicator of the magnitude of the observed differences (Funder & Ozer 2019; Sullivan & Feinn 2012; Wilkinson and Task Force on Statistical Inference, American Psychological Association, Science Directorate, 1999).”
Wong, S., Wong, P., Leung, Y., & Chun, H.-T.-A. (2026) · Frontiers of Nursing · doi:10.2478/fon-2026-0023
Caro, M. P. P., Arbulu, A. A., Jurado, J. M. L., Bermejo, J. V. D., Álvarez, J. F., & González, F. J. N. (2026) · Journal of Dairy Science · doi:10.3168/jds.2025-27851
Gao, X. (2026) · Journal of Interpersonal Violence · doi:10.1177/08862605261451866
“While some effect sizes were modest, their interpretation should consider contextual and population-level relevance rather than statistical significance alone (Sullivan & Feinn, 2012).”
Boukhechba, M., Li, Z., Reynoso, E., Pandis, I., Mosca, K., Morris, M., & Avey, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1759967
“As reported in the literature, 0.2, 0.50, and 0.8 are used as thresholds to interpret small, medium, and large effect (41).”
InfluentialMercado, M., Martín-Márquez, B., Martínez-García, E., & Petri, M. H. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48060561
“In genetic studies of rare diseases, borderline significance in small sample sizes frequently serves as an exploratory signal that requires cautious interpretation, rather than definitive proof of association [16,17].”
Analisis Komparatif Model Random Forest dan XGBoost Berdasarkan Kinerja AUC Pada Fraud Detection
Saidah, M. A., & Saputra, A. (2026) · Techno.Com · doi:10.62411/tc.v25i2.16035
“Penggunaan effect size penting karena signifikansi statistik tidak selalu menunjukkan besarnya pengaruh praktis dari suatu perbedaan [30].”
Anders, C., Steiniger, B., Saenger, F., Marks, M., Mader, L., Dukvin, E., & Schneider, A. (2026) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk11020212
“Furthermore, with regard to effect size, differences were considered relevant only if ES was at least 0.5 [46].”
InfluentialDigital Skill Disparities across Levels of Urbanization in the European Union
Gontkovičová, B., Spišáková, E. D., & Bocánová, S. (2026) · TEM Journal · doi:10.18421/tem152-90
“Since p-values alone do not indicate the magnitude of the effect [28], practical significance was also assessed using Cohen’s d. Effect sizes are conventionally interpreted as small (d = 0.2), medium (d = 0.5), and large (d ≥ 0.8) [29].”
InfluentialCerfoglio, S., Vismara, L., Priano, L., Mauro, A., Bigoni, M., Galli, M., & Cimolin, V. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1807498
“Effect sizes (Cohen’s d) were computed to quantify the magnitude of between-group differences and interpreted according to Cohen’s criteria as small ( d ≤ 0.2), moderate (0.2 d 0.8), large (0.8 ≤ d 1.3), and very large ( d ≥ 1.3) (48).”
InfluentialRana, E. (2026) · Arquivos de Neuro-Psiquiatria · doi:10.1055/s-0046-1829166
Can Plasma Exchange Be Used to Lower the Circulating Burden of Microplastics in Human Patients?
Weinstein, R., Yüksel, Z. S., Anderson, C., Grant, D. G., Milius, P., Waldron, K., David, L., Molina, C., Ruhoy, I. S., Kaufman, D. L., et al. (2026) · Journal of clinical apheresis · doi:10.1002/jca.70135
Wang, J.-R., & Ma, Y. (2026) · BMC Ophthalmology · doi:10.1186/s12886-026-04927-1
“The confidence interval (CI) including zero precludes definitive conclusions regarding equivalence or non-inferiority [22].”
Günver, M., Sert, M. F., Yurtseven, E., Sezgin, M., Sayılı, U., Canbaz, M., Çulpan, H. C., Can, G., Erginöz, E., Yusuf, M., et al. (2026) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365513.2026.2681040
“In the interpretation of clinically meaningful or practice-relevant differences, additional parameters such as effect size and confidence intervals should therefore be considered alongside p -values [9,10].”
InfluentialHaverila, M., Currie, R., & Pal, A. (2026) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-12-2025-1111
Implementation of a Validated Digital Competence E-Learning Program in Graduate Nursing Students
Adif, S. A., Yen, M., Natashia, D., Tran, L. T. K., & Hadi, M. (2026) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001561
Abeles, R., Eger, L., Slayden, T. A., Charat, S., & Maharaj, S. K. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09419-w
“We categorized effect sizes as small (0.2), medium (0.5), large (0.8), and very large (≥ 1.3) [21].”
InfluentialDoan, H. T., Phan, N. T. D., Ho, H. T., Nguyen, L. T. T., Khánh, Đ. N., Nguyen, O. T. P., & Trinh, N. B. (2026) · PLOS Global Public Health · doi:10.1371/journal.pgph.0006385
Zijp, T., Nolte, S., Knobbe, T., de Meijer, V. D., Gan, C., Bakker, S. J. L., Sommer, I., de Vries, S. D., & Touw, D. (2026) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2026.2671063
“Interpretation of differences between sex and age groups in the outcomes was done using the Cohen’s D effect size according to weighing as previously described [48,49].”
Neonatal assessment and VR: designing 3D simulation training for prehospital clinicians
Hill, M. G., Hopper, L., Flanagan, B., Hansen, S. S., Miles, A. K., Watson, J., Cashman, S., Balding, K. J., Dysktra, P., Johnson, J., et al. (2026) · Virtual Reality · doi:10.1007/s10055-026-01382-7
“Cohen’s effect size looks at the strength of a relationship between two variables, with 0.2 having a small effect; 0.5 being a medium effect; 0.8 having a large effect and 1.3 a very large effect (Sullivan and Feinn 2012).”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
