Software hands you a table of numbers. This page turns each one — coefficient, p-value, confidence interval, R², odds ratio — into a sentence you can defend.
Here is a real-shaped output: we explain a satisfaction score (0–100) from three drivers. Tap any row and the panel translates that row into plain English. Tap the fit chips below too.
Interactive 1 — tap each row and each chip.
Variable
Coefficient
Std. error
p-value
95% CI
Perceived price
−4.20
1.05
0.001
[−6.3, −2.1]
Wait time
−2.80
0.95
0.004
[−4.7, −0.9]
Loyalty member
+0.90
0.72
0.210
[−0.5, +2.3]
R² = 0.62Adj. R² = 0.60n = 240F-test p < 0.001
Tap a row or chip…
2 · WHAT A p-VALUE REALLY IS
THE MOST MISUNDERSTOOD NUMBER IN RESEARCH
The logic of "could this be a fluke?"
Start from a sceptical position called the null hypothesis: "this variable has no real effect; its true coefficient is 0." Your data shows some effect — but random sampling alone produces apparent effects even when none exist. The p-value answers one precise question:
p = P( seeing an effect this big or bigger | the truth is zero )
small pthe data would be very surprising if the effect were really zero → evidence the effect is real. By convention p < 0.05 is "statistically significant."
large pthis much apparent effect happens easily by chance → you cannot rule out zero.
Below: the bell curve is what the coefficient would look like across many samples if the true effect were zero. Slide your observed coefficient outward. The shaded tails are the p-value — the chance of landing that far out by luck alone.
Interactive 2 — push the observed effect away from zero; watch p shrink.
p-value (two-tailed)
—
Verdict at α=0.05
—
What p is NOT. It is not the probability the hypothesis is true. It is not the size or importance of the effect (a tiny, useless effect can be "significant" with a big enough sample). And p > 0.05 does not prove "no effect" — only that you couldn't detect one. Report effect sizes and CIs, never p-values alone.
3 · CONFIDENCE INTERVALS
THE NUMBER EXAMINERS PREFER TO p
A range of plausible truths
A coefficient of −4.2 is your single best estimate, but it has uncertainty. The 95% confidence interval [−6.3, −2.1] gives the range of values consistent with your data. The interpretation that matters:
If the CI excludes 0, the effect is significant at the 5% level — the same verdict as p < 0.05, but more informative because it shows the plausible magnitude.
A narrow CI = a precise estimate (usually more data). A wide CI = high uncertainty; be cautious.
"95%" refers to the method: across many repeated samples, 95% of such intervals would contain the true value.
Slide the sample size and watch the CI tighten around the estimate — and watch a borderline effect cross from "not significant" to "significant" purely because precision improved.
Interactive 3 — more data narrows the interval.
Estimate
−2.0
95% CI
—
Significant?
—
4 · ODDS RATIOS (LOGISTIC OUTPUT)
A DIFFERENT TABLE, A DIFFERENT RULE
Logistic regression reports odds ratios
Because logistic models a probability through the log-odds, its coefficients are exponentiated into odds ratios (OR). The single rule:
OR > 1 raises the odds of "yes" · OR < 1 lowers them · OR = 1 means no effect. A CI for an OR signals significance when it excludes 1 (not 0 — because 1 is the "no effect" point on the odds scale).
Tap each row of this churn model.
Interactive 4 — tap each row. Watch where the "no effect" line (OR=1) falls.
Variable
Odds Ratio
p-value
95% CI for OR
Filed a complaint
2.50
0.002
[1.4, 4.5]
Tenure (per year)
0.70
0.010
[0.55, 0.90]
Newsletter member
1.05
0.480
[0.85, 1.30]
Tap a row…
Reading these two tables — coefficients with CIs, and odds ratios — is the bulk of what any examiner will ask you to interpret.
Scroll progress saves on this device. Part of the DBA Data-Analysis series by Jan Erik Meidell.