Reporting statistics in APA style means stating each result in a fixed order: the test, its degrees of freedom, the test statistic, the exact p-value, and an effect size. The format is rigid on purpose, so any reader can judge your result at a glance. Most marks lost in a results chapter come not from the analysis but from reporting it inconsistently.
The pattern behind every APA statistic
Once you see the underlying pattern, every test follows it: name the statistic in italics, give the degrees of freedom in parentheses, then the value, then significance, then effect size. Numbers that can exceed 1 take a leading zero; p-values and correlations, which cannot, do not. Report p exactly (p = .03), not as p < .05, unless it is below .001, in which case you write p < .001. To apply all of these rules at once, the APA results formatter turns a raw t, F, chi-square, or correlation result into the finished sentence.
Templates for the common tests
Match your test to the line below and substitute your own numbers:
| Test | APA reporting format |
|---|---|
| Independent t-test | t(48) = 2.34, p = .02, d = 0.68 |
| One-way ANOVA | F(2, 87) = 5.12, p = .008, η² = .11 |
| Pearson correlation | r(98) = .42, p < .001 |
| Chi-square | χ²(1, N = 120) = 6.71, p = .010 |
| Linear regression | β = .35, t(96) = 3.21, p = .002 |
Always report an effect size, not just significance
A p-value tells you whether an effect is likely real; an effect size tells you whether it is large enough to matter. APA expects both, and reviewers increasingly treat a missing effect size as a flaw. Use Cohen's d for mean differences, eta-squared for ANOVA, and the correlation coefficient or R-squared for relationships. A significant result with a tiny effect size deserves a cautious interpretation, not a celebration.
Tables, figures, and the text that surrounds them
Do not make a reader hunt through a table for your main finding. State the key result in the text, then use tables for the full detail and figures for patterns the eye reads faster than numbers. Every table and figure needs a number, a title, and enough notes to stand alone. Round consistently, usually to two decimal places, and keep the same precision across the chapter.
Reporting confidence intervals and regression tables
A confidence interval belongs next to almost every estimate, because it shows the precision behind the point value. Present it in brackets right after the statistic, for example a mean difference of 4.20, 95% CI [1.85, 6.55]. The interval communicates more than a p-value alone: a narrow band signals a precise estimate, while a wide one that straddles zero warns the reader that the effect could be negligible. APA expects the interval reported to the same number of decimal places as the estimate it accompanies.
A regression table is where reporting most often falls short. A complete one gives, for each predictor, the unstandardised coefficient B, its standard error SE, the standardised coefficient beta, the t value, and the exact p. Below the predictors, report the model's R-squared and its significance so the reader can judge how much variance the model explains. Leaving out the standard errors or the model fit makes the table impossible to evaluate, and your committee will ask for both.
APA reporting errors markers flag
Most marks lost in this area come from a short list of avoidable slips:
- Leading zeros on p-values: a p-value cannot exceed 1, so it takes no leading zero; write p = .04, not p = 0.04.
- Inconsistent decimal places: shifting between two and three decimals across the chapter looks careless and obscures comparison.
- Missing effect sizes: reporting significance with no Cohen's d, eta-squared, or R-squared leaves the practical size of the finding unstated.
- Stating p = .000: software rounds to zero, but a probability is never exactly zero; report p < .001 instead.
- Reporting significance without interpretation: a number with no sentence explaining what it means for the research question is an incomplete result.
Interpret, do not just list
The weakest results chapters read as a list of p-values with no meaning attached. After each result, say in one sentence what it means for your research question. That habit is what separates a pass from a strong chapter, and it is the same standard expected at doctoral level under PhD statistics help. Before you report anything, make sure the test was valid in the first place by checking its assumptions, and if you are unsure which test produced the cleanest answer, revisit choosing a statistical test.