Reporting effect sizes and confidence intervals means showing how large and how precise your result is — not just whether it cleared a p-value threshold.
Reporting effect sizes and confidence intervals means showing how large and how precise your result is — not just whether it cleared a p-value threshold. For Vietnamese researchers preparing a first Q1 or Q2 submission, this habit often separates a manuscript that reads as statistically mature from one that draws a "revise your reporting" comment.
Effect sizes and confidence intervals are the language international-journal reviewers now expect. This guide answers the seven questions Vietnamese researchers ask MAAS publishing mentors most often when moving from a p-value-only write-up to a reportable results section.
Author: MAAS Research Methods & Statistics Desk · Reviewed by a Principal Publishing Advisor (PhD, Scopus Q1 author and reviewer)
Last updated: 2026-07-07
Category: research-methods
What does it mean to report an effect size and a confidence interval?
Direct answer: An effect size is a number that expresses the magnitude of a result — how big a difference or how strong a relationship is — on a standardised or meaningful scale. A confidence interval (CI) gives the range of plausible values for that effect. Together they answer "how much" and "how sure," where a p-value only answers "is it unlikely to be zero."
Evidence: Sullivan and Feinn (2012) argue that a p-value tells you only whether an effect exists, not how large it is, and that effect size is the "main finding of a quantitative study." Nakagawa and Cuthill (2007) show that pairing an effect size with its confidence interval communicates both magnitude and precision in one statement — something a significance test cannot do.
Example: A MAAS Publishing Advisory client in nursing reported only "the intervention group improved significantly (p = 0.02)." Her mentor helped her re-express it as a mean difference of 4.1 points, 95% CI [0.8, 7.4], Cohen's d = 0.52 — so reviewers could now see the effect was moderate and estimated with real uncertainty.
Why isn't a p-value enough on its own?
Direct answer: A p-value depends heavily on sample size, so a trivial effect can be "significant" in a large study and an important effect "non-significant" in a small one. Reporting the effect size and CI decouples practical importance from sample size — exactly what editors and reviewers now expect you to demonstrate.
Evidence: The American Statistical Association's formal statement warns that a p-value does not measure the size of an effect and that "scientific conclusions should not be based only on whether a p-value passes a specific threshold" (Wasserstein & Lazar, 2016). Cumming (2014) makes the same case under "the new statistics," urging authors to report estimates and intervals rather than lean on dichotomous significance.
Example: A Vietnamese doctoral candidate in education had a survey dataset where almost every correlation was "significant." Her MAAS mentor showed many had r values near 0.08 — significant but practically negligible. Reporting effect sizes let her foreground the three relationships that mattered, and reviewers praised the restraint.
Which effect size should you report for your statistical test?
Direct answer: Match the effect size to your analysis: standardised mean differences for group comparisons, correlation-family measures for associations, and variance-explained measures for models. Report the effect size your target journal's field uses most often, and state which one you chose so readers can interpret it correctly.
Evidence: Lakens (2013) provides a practical primer mapping t-tests and ANOVAs to Cohen's d and eta-squared, and stresses reporting the specific estimator used. Cohen's (1988) original framework remains the reference point for the standardised families still cited across disciplines.
Example: A MAAS-coached management researcher reported only beta coefficients from a regression. Her mentor added R² for the model and standardised betas for each predictor, so the paper communicated both overall explanatory power and the relative weight of each variable — the depth her Q2 target journal expected.
The table below maps common analyses to the effect size most reviewers expect.
| Your analysis | Common effect size | What it expresses |
|---|---|---|
| Two-group comparison (t-test) | Cohen's d (or Hedges' g) | Standardised difference between two means |
| Comparison of 3+ groups (ANOVA) | Eta-squared (η²) or omega-squared (ω²) | Proportion of variance explained by the factor |
| Correlation between two variables | Pearson r (or Spearman ρ) | Strength and direction of an association |
| Regression model | R² and standardised β | Variance explained; weight of each predictor |
| 2×2 categorical outcome | Odds ratio or risk ratio | Relative likelihood of an outcome |
| Non-parametric group comparison | Rank-biserial r or Cliff's delta | Standardised difference without normality |
How do you report a confidence interval correctly?
Direct answer: State the confidence level (usually 95%), give the point estimate first, then the interval in square brackets — for example, d = 0.52, 95% CI [0.14, 0.90]. Interpret the width as precision: a narrow interval means a well-estimated effect, a wide one means your study cannot pin the effect down yet. Never describe a CI as "the probability the true value lies inside it."
Evidence: Nakagawa and Cuthill (2007) recommend reporting confidence intervals for every key effect and reading their width as the study's precision. Cumming (2014) shows that intervals overlapping zero convey the same information as a non-significant test, but with far more nuance about plausible effect magnitudes.
Example: A MAAS mentor reviewed a psychology manuscript reporting d = 0.6 with no interval. Adding 95% CI [−0.05, 1.25] changed the story: the wide interval crossing zero revealed the study was underpowered, so the author reframed the result as preliminary rather than confirmatory.
How do you interpret effect size magnitude without misusing Cohen's benchmarks?
Direct answer: Cohen's labels — roughly 0.2 small, 0.5 medium, 0.8 large for d — are a starting point, not a verdict. Interpret magnitude against effects typical in your own field and the practical stakes of your outcome. A "small" d can be important in a clinical or educational intervention; a "large" d can be trivial if the outcome barely matters.
Evidence: Cohen (1988) himself intended the thresholds as conventions to be used only when field-specific benchmarks were unavailable. Sullivan and Feinn (2012) caution that mechanical labelling misleads readers and urge authors to interpret effects in the context of prior studies and real-world relevance.
Example: A Vietnamese public health researcher found a d of 0.3 for a low-cost behaviour-change nudge and worried it was "too small to publish." Her MAAS mentor benchmarked it against comparable trials, where effects of 0.2 to 0.3 were the norm and valued because the intervention was cheap and scalable. Framed that way, the modest effect became the paper's selling point.
Where in a Q1 paper do effect sizes and confidence intervals belong?
Direct answer: Put the primary effect size and CI in the abstract, restate them for every key comparison in the results, and reference them again when you weigh practical importance in the discussion. Keep the raw statistics (test statistic, df, exact p) alongside them, so the reader sees significance and magnitude together rather than choosing between the two.
Evidence: The SAMPL guidelines direct authors to report the magnitude of effects with confidence intervals and to give exact p-values rather than thresholds (Lang & Altman, 2015). Leading the abstract with the estimate and interval makes the headline finding a magnitude, not a verdict.
Example: A MAAS Publishing Advisory client's abstract originally said only "differences were significant." Her mentor rewrote the key sentence to "the programme raised scores by 4.1 points (95% CI [0.8, 7.4], d = 0.52)." Mirrored across results and discussion, that change led a reviewer to call the reporting "commendably transparent."
What are the most common effect size reporting mistakes reviewers flag?
Direct answer: The frequent errors are: reporting significance with no effect size; giving an effect size but no confidence interval; mixing incompatible measures across the paper; treating Cohen's labels as findings; and rounding intervals so aggressively they lose meaning. Each is cheap to fix before submission and costly to fix after a reviewer catches it.
Evidence: Lang and Altman (2015) list omission of effect magnitude and of confidence intervals among the most common statistical reporting failures in biomedical journals. Wasserstein and Lazar (2016) specifically flag over-reliance on the 0.05 threshold in place of estimation as a recurring problem.
Example: A MAAS mentor guided a Vietnamese postgraduate through an Outline → Draft → Final review of her statistics: the outline fixed which effect size went with each hypothesis, the draft added every confidence interval, and the final pass standardised the format across abstract, tables, and text. The student stayed the author throughout, with the mentor advising rather than running the analysis.
Frequently asked questions
Do I need to report an effect size for every statistical test?
Report an effect size for every result you interpret as a finding — each key comparison, correlation, or model. You do not need one for purely descriptive checks such as a normality test, but any result you discuss as evidence should carry a magnitude and, wherever possible, a confidence interval.
What confidence level should I use, 95% or 99%?
Use 95% unless your field or journal specifies otherwise, because it is the near-universal default and lets reviewers compare your intervals with the wider literature. A higher level such as 99% produces wider intervals; only switch if you have a stated reason, and report the level explicitly every time.
Can I report a confidence interval instead of a p-value?
You can, and many statisticians encourage leading with the interval, but most journals still expect exact p-values alongside estimates. The safest approach is to report the effect size, its confidence interval, and the exact p-value together, so significance and magnitude are visible in the same line.
How do I get effect sizes and confidence intervals out of my software?
SPSS, R, Stata, and JASP all produce effect sizes and intervals, sometimes as an option you must switch on. In R, packages in the effect size family return standardised measures with intervals; recent SPSS versions print them in the output. State the software and version in your methods section.
Is an effect size still meaningful if my result is not statistically significant?
Yes. A non-significant result still has an estimated effect size and a confidence interval, and reporting them is more honest than saying "no effect." A wide interval crossing zero tells readers your study could not resolve the effect, which is useful information for future meta-analysis.
Can MAAS help me report effect sizes and confidence intervals for my Scopus paper?
Yes. MAAS Publishing Advisory coaches Vietnamese researchers through analysis planning, effect size selection, confidence interval reporting, and results-section writing using the Outline → Draft → Final model, with developmental feedback from PhD-level mentors. Book a consultation through our contact page.
Ready to make your statistics Q1-ready?
Effect sizes and confidence intervals are decided when you plan the analysis, not bolted on at the end — and far easier to get right with a mentor who has judged papers from the reviewer's side. MAAS Publishing Advisory pairs you with a PhD-level mentor — 23% of our experts hold doctorates — for a free 20-minute consultation, matches you to the right advisor within 48 hours, and backs every engagement with our three-tier Pass / Merit / Distinction guarantee and a 90-day post-submission warranty. We coach; you stay the author, every step.
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Related guides
- How do you choose the right statistical test for a Q1 paper? — pick the analysis before you report its effect size
- How do you calculate the sample size for a research study? — power your study so effects are estimated precisely
- How do you write the results section of a research paper? — where effect sizes and intervals live in the paper
- What reporting guidelines should you follow for a research paper? — the checklists that require effect sizes
- Publishing Advisory service — full service tiers for Scopus Q1/Q2 support
- Data & Coding Projects service — statistical analysis support in SPSS, R, and Python
- Scopus publishing resource hub — the full roadmap from analysis to acceptance
- Meet the MAAS experts — the PhD-level mentors behind our publishing advisory
References
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
- Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966
- Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863. https://doi.org/10.3389/fpsyg.2013.00863
- Lang, T. A., & Altman, D. G. (2015). Basic statistical reporting for articles published in biomedical journals: The "Statistical Analyses and Methods in the Published Literature" or the SAMPL guidelines. International Journal of Nursing Studies, 52(1), 5–9. https://doi.org/10.1016/j.ijnurstu.2014.09.006
- Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological Reviews, 82(4), 591–605. https://doi.org/10.1111/j.1469-185X.2007.00027.x
- Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
This article is part of the MAAS Journal series for Vietnamese international postgraduate students and researchers. MAAS Publishing Advisory is an advisory partner — we coach authors through the Outline → Draft → Final delivery model with developmental feedback from PhD-level, Scopus-published mentors. We do not write, submit, or guarantee acceptance of work on an author's behalf.