Statistical Reporting Standards: Transparent Reporting of Analyses
Guidelines for Reporting Statistical Analyses and Results · Also known as: reporting statistics, statistical transparency, effect size reporting
Transparent reporting of statistical results—including effect sizes, confidence intervals, p-values, and assumptions—is essential for scientific integrity and reproducibility. Many published studies report p-values in isolation without effect sizes or confidence intervals, making it impossible for readers to assess the magnitude of findings. Statistical reporting standards, emphasized by Cumming (2013), the American Statistical Association, and the ICMJE, require effect sizes, confidence intervals, and discussion of uncertainty. This enables readers to judge whether findings are practically significant (not just statistically significant) and to compare effect sizes across studies in meta-analyses. Poor statistical reporting wastes research and prevents proper synthesis of evidence.
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When to use it
Report statistics transparently in all empirical research: observational studies, experiments, surveys, clinical trials, meta-analyses. This applies to primary analyses and sensitivity/subgroup analyses. For descriptive studies without hypothesis tests (e.g., descriptive surveys), report descriptive statistics, ranges, and proportions clearly. For qualitative research, statistical reporting does not apply, but describe the sample and analysis method transparently. For modeling or simulation studies, report effect sizes and confidence intervals around key outcomes.
Strengths & limitations
- Enables interpretation of magnitude: readers see not just 'significant difference' but 'the difference is d=0.47' (medium effect), crucial for judging practical significance.
- Facilitates meta-analysis: effect sizes allow studies to be combined; p-values alone are insufficient for meta-analysis.
- Reduces p-hacking bias: when effect sizes are reported, readers can see when effects are trivial despite p<0.05, reducing incentive for researcher degrees of freedom.
- Improves reproducibility: confidence intervals and assumptions checking enable readers to evaluate sensitivity and validity of findings.
- Supports post-hoc power analysis: reported effect sizes allow calculation of statistical power, revealing whether underpowered studies drove null findings.
- Requires space: comprehensive statistical reporting (effect size, CI, p-value, assumptions) takes more space than 'p<0.001' alone; journal limits may squeeze other content.
- Complexity: readers unfamiliar with effect sizes may find comprehensive reporting hard to interpret; education is needed.
- Interpretation ambiguity: 'meaningful' effect size depends on context (d=0.2 is small in some fields, large in others); no universal standard.
- Multiple comparisons inflate false positives: even with corrections, many hypothesis tests increase Type I error; comprehensive reporting does not solve this, only documents it.
Frequently asked
What effect size should I report?
Choose the effect size appropriate to your statistical test: For t-tests (two-group comparison): Cohen d (standardized mean difference) or the raw difference with units. For ANOVA: eta-squared (η²) or partial eta-squared (η²p). For correlation: Pearson r or r-squared (R²). For binary outcomes (e.g., treatment success): odds ratio (OR), relative risk (RR), or absolute risk difference. For paired data: Cohen d or Hedge's g (similar to d, used in meta-analyses). If unsure, consult field-specific standards or reporting guidelines (EQUATOR Network).
Is it enough to report just the p-value?
No. The p-value alone is insufficient because it does not tell you the magnitude of the effect. A p=.001 could reflect a trivial effect (if the sample is large) or a huge effect (if the sample is small). Always report effect size and 95% CI. The combination of p-value + effect size + CI gives readers the full picture.
What if I have many tests and several p-values are 'close to significant' (e.g., p=.06)?
Report them honestly. A p=.06 is not statistically significant at α=0.05; it does not become significant by calling it a 'trend.' Discuss what the non-significant result means in context (lack of power? true null effect?). If you conducted multiple tests, report how you controlled for Type I error (Bonferroni, FDR, etc.). Pre-register hypotheses to distinguish confirmatory from exploratory analyses.
How many decimal places should I use for p-values?
Two to three decimal places is standard (e.g., p=.021, p<.001). Do not report p=.050000. Exact p-values are preferred to inequalities (p=.021 rather than p<.05) when the exact value is available from your statistical software. Use 'p<.001' only if the software rounds smaller values or if the test is one-tailed with a very small threshold.
Should I report 95% CI or other confidence levels?
Use 95% CI unless your field has a different convention (some older medical literature used 90% or 99%). The 95% CI is the standard because it aligns with α=0.05. If you report a 90% CI, explicitly state 'CI90%' to avoid confusion. Do not mix confidence levels in the same paper.
Sources
- Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29. DOI: 10.1177/0956797613504966 ↗
- Fidler, F., Thomason, N., Cumming, G., Finch, S., & Leeman, J. (2005). Editors can lead researchers to confidence intervals, but can't make them think: Statistical reform lessons from medicine. Psychological Science, 15(2), 119–126. DOI: 10.1111/j.0963-7214.2004.01502008.x ↗
- International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. link ↗
How to cite this page
ScholarGate. (2026, June 3). Guidelines for Reporting Statistical Analyses and Results. ScholarGate. https://scholargate.app/en/academic-writing/statistical-reporting-standards
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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