Statistics are where many manuscripts are won or lost in review. Reviewers read the numbers closely, and nothing undermines confidence faster than a results section that leans on a single p-value and calls it a day. Reporting statistics well is not about advanced methods — it is about giving the reader enough, honestly, to judge what you found and to reproduce it. A few principles cover most of it.
Report the effect, not just the p-value
The most important number is usually the effect size — how big the difference or relationship actually is — because that is what matters in the real world. A study can be statistically significant and practically meaningless: with a large enough sample, a trivial difference clears p < 0.05. Always report the effect size (a mean difference, an odds ratio, a correlation) so readers can judge whether it matters, not just whether it reached a threshold.
Use the p-value for what it is
A p-value is widely misread. It is the probability of seeing data at least as extreme as yours if the null hypothesis were true. It is not the probability that your hypothesis is correct, and it says nothing about the size of an effect. Report exact values (p = 0.03, not "p < 0.05") except for very small ones, and never treat 0.049 and 0.051 as fundamentally different — they are not.
Give a confidence interval
A confidence interval shows the range of plausible values for your effect, and it does far more work than a p-value alone. "A 4.2-point reduction (95% CI 1.1 to 7.3)" tells the reader both the estimate and its uncertainty at a glance. Where you can, report the interval with every key estimate.
Don't dichotomise the world
Splitting every result into "significant" and "not significant" throws away information and encourages bad inference. A non-significant result is not proof of no effect — it may just be an underpowered study. Describe your findings on a continuum: the size of the effect, the uncertainty around it, and what that means, rather than a binary verdict.
Report enough to reproduce
Finally, give the mechanics. For every test, state which test you used, the sample size, the test statistic where relevant, and the exact p-value. If you ran many comparisons, say so and how you handled it. This detail is not padding — it is what lets a reader (or a reviewer) check your work, and it is central to reproducibility.
A simple rule of thumb: for each key result, report the effect size, its confidence interval, and the exact p-value — in that order of importance.