Writing a statistical result the way a journal expects
Test statistic, degrees of freedom, p-value, effect size — in that order, in a sentence.
A reported result carries the direction and size of the difference, the test that was used, its statistic and degrees of freedom, the p-value, and an effect size. It reads as a sentence, not as a table dropped into the prose.
Why it matters
An examiner should be able to reconstruct what you did from the sentence alone. Chavery produces these sentences from the computed values by rule, which is why the same result always yields the same wording — and why it can be checked.
An example
'Mean pain reduction was significantly greater in the supervised group (4.2 ± 1.6) than in the home exercise group (2.9 ± 1.8), t(40) = 2.47, p = .018, Cohen's d = 0.76 (95% CI 0.13 to 2.47).'
For a chi-square: 'Improvement was more common in the supervised group (18/21, 85.7%) than the home group (11/21, 52.4%), χ²(1) = 5.56, p = .018, Cramér's V = 0.36.'
Common mistakes
- 'The result was significant.' Which result, by how much, on what test?
- Writing p = .000 rather than p < .001.
- A leading zero on a p-value. Convention is p = .018, not p = 0.018, because a p-value cannot exceed 1.
- Reporting the same numbers in a table and again in full in the text. The text should point at the table and say what it means.
Read next
- What a p-value is, and what it is not — The single most misreported number in postgraduate research.
- Effect size — how big, not just whether — Significance tells you something is there. Effect size tells you whether it matters.
- Describing your data before you test anything — Mean and SD, or median and IQR — and how to tell which.
Chavery Research Companion applies this to your own study: it asks the questions in plain language, checks the assumptions against your data, recommends the test, and writes the sentence that reports it. Start free — planning and the master chart cost nothing.