Quantitative analysis
“I don’t understand my own statistics. Where do I start?”
Reading the few things that change your conclusion, in plain words.
You ran the analysis, or someone ran it for you, and the output is a wall of terms — regression, effect size, post-hoc, MANCOVA. Half of it you don’t follow, so you have quietly trusted the conclusions. You do not need to become a statistician. You need to read four things.
“I am a PhD student, and I don’t understand statistical studies. Ok… I said it. Since I don’t understand half of the results, I have to trust in the correctness of the conclusions.” — a doctoral student, saying it out loud
The problemTrusting a conclusion you can’t read is a risk, not a shortcut.
The honest thing that student admitted — trusting the conclusions because the results are opaque — is extremely common, and it is where errors hide. If you cannot read your own output, you cannot tell a strong finding from a fragile one, and neither can you catch the mistake that turns a significant result non-significant. The good news: for most theses, four ideas carry nearly all the meaning, and none of them needs an equation.
Why it mattersYou will have to defend numbers you did not fully read.
At a viva or in review, someone will ask what a result means — not how the software computed it. “What does this effect size tell us in practice?” is answerable in a sentence if you have read the four things, and terrifying if you have not. Reading your own results is also the only way to notice when the analysis is wrong before a reviewer does.
Common mistakesFour misreadings to avoid
- Reading significance as importance. “p < 0.05” says the effect is probably not zero, not that it is large or that it matters.
- Reading non-significance as “no effect”. It often means the study was too small to be sure — absence of evidence, not evidence of absence.
- Ignoring the confidence interval. The interval, not the point estimate, tells you how much you actually know.
- Reporting only the p-value. A finding without an effect size and interval is half a sentence — reviewers notice the missing half.
The four thingsWhat actually changes the meaning
1 · Effect size — how big. This is the number that matters most and gets read least. It answers “how much?” — how large the difference or association is, in units you can picture. A tiny effect can be statistically significant in a big sample and still be meaningless in practice.
2 · Significance (the p-value) — how surprising if nothing were going on. A small p-value says your result would be unlikely if there were truly no effect. It does not say the effect is big, important, or definitely real. Treat it as one clue, read alongside the effect size, never on its own.
3 · The confidence interval — the range of what’s plausible. Read the interval as “values compatible with my data”, not a verdict. A narrow interval means you have pinned the effect down; a wide one crossing zero means you genuinely do not know the direction yet. The interval is where your certainty actually lives.
4 · What to report. For most results, report the effect size, its confidence interval, and the p-value together, in that order of importance — and say what the effect means in plain terms. That single habit answers most viva questions before they are asked.
Worked exampleReading one result aloud
From output to a sentence
Output: mean difference 2.1 points, 95% CI 0.3 to 3.9, p = 0.02.
Read plainly: “The intervention group scored about 2 points higher on average. The data are compatible with a true difference anywhere from about 0.3 to 3.9 points, so the direction is fairly clear but the size is uncertain. The p-value of 0.02 means a difference this large would be unlikely if there were really no effect. Whether 2 points matters is a judgement about the scale, not a statistical one.”
What you just did: you read the size, the range, and the significance, and separated “is it there?” from “does it matter?” — which is the whole skill.
Checklist & one-pagerRead any result
Downloadable · read-your-results one-pager
The four-things one-pager
- Effect size: how big is it, in units I can picture?
- Does the size matter in practice, on this scale?
- Confidence interval: how wide, and does it cross zero (or 1 for ratios)?
- p-value: read alongside the effect size, not alone.
- Have I separated “is the effect there?” from “does it matter?”
- Can I say the result in one plain sentence?
- If anything still doesn’t read, flag it for the statistician — early.
The honest limit. Reading these four things helps you understand and report your results; it does not replace a statistician for study design, model choice, or a complex analysis. Knowing which four things to read is exactly what lets you ask the statistician a precise question instead of a panicked one.
When to call the statisticianSooner than you think
Ask for help — early, and specifically — when the model itself is in question (which test, which covariates), when results shift depending on choices you don’t understand, or when a reviewer challenges the analysis. Bring the four things you can read and name exactly what you can’t. “I can read the effect size and interval, but I don’t understand why this covariate changes the result” gets you a useful answer; “I don’t understand any of it” gets you a lecture.
Related guidesRead next
If your result is a causal claim, the assumptions under it are worth a look before you defend it — AssessLite stress-tests those assumptions and records what held, failed, or could not be resolved. That is a next step for causal work, not a substitute for reading the four things above.
Further readingSources worth your time
- Greenland et al. (2016), Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations — European Journal of Epidemiology.
- Wasserstein & Lazar (2016), The ASA statement on p-values — The American Statistician.