CRITICAL APPRAISAL

How to Interpret the Results Section of a Research Paper

Guide · 1 min read · Last updated 2026-10-31

To interpret the Results section, first find the primary outcome and its effect size with a confidence interval, check the participant flow and baseline table, then judge whether secondary and subgroup findings are described as exploratory. Do not rely on p-values alone.

What to look for

  • Participant flow: how many were screened, included and analysed; how many were lost.
  • Baseline table: are groups comparable? Imbalance suggests confounding in observational studies or chance in trials.
  • Primary outcome: the pre-specified result, with effect size (for example a risk ratio, mean difference) and 95% confidence interval.
  • Absolute numbers: event rates in each group, which make effects easier to understand.
  • Secondary and subgroup analyses: more tests mean more chance of false positives.
  • Missing data and adjustments: what was done and how many were affected.

Reading a result sentence

"Mean systolic pressure fell by 6.2 mm Hg more in the intervention group (95% CI 2.1 to 10.3; p = 0.003)." The estimate is 6.2; plausible values range from 2.1 to 10.3; the p-value suggests the data are unlikely if the true difference were zero, but you must also ask whether a 2 to 10 mm Hg difference is clinically important. See Statistical Significance vs Clinical Significance.

Illustrative example

Invented for teaching. A trial reports "significant improvement" in 1 of 12 secondary outcomes (p = 0.04) but not in the primary outcome (p = 0.30). The honest summary is that the primary outcome showed no clear effect and the secondary finding is exploratory and may be chance.

Common pitfalls

  • Reading only "significant" results.
  • Ignoring confidence intervals.
  • Treating subgroup differences as proof without an interaction test.

Key takeaways

  • Start with the primary outcome, effect size and interval.
  • Check flow, baseline balance and missing data.
  • Treat secondary and subgroup results cautiously.

References and further reading

  1. Wasserstein RL, Lazar NA. The ASA statement on p-values: context, process, and purpose. Am Stat. 2016;70(2):129-133. doi:10.1080/00031305.2016.1154108
  2. Altman DG, Bland JM. Standard deviations and standard errors. BMJ. 2005;331:903. doi:10.1136/bmj.331.7521.903
  3. Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31:337-350. doi:10.1007/s10654-016-0149-3
  4. Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. doi:10.1136/bmj.c332

Related reading

This page is general educational information about research methods. It is not medical, legal or regulatory advice; follow your institution's ethics board and your target journal's instructions.