Q-Q Plot
Chart overview
A Q-Q plot compares the quantile function of observed data against that of a reference distribution (typically normal), with deviations from the diagonal indicating skewness, heavy tails, or multimodality.
Key points
- It is a standard diagnostic in regression analysis, hypothesis test validation, and data quality assessment.
- Researchers use it to decide whether parametric tests are appropriate and to characterize the nature of distributional departures.
Example Visualization
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"Create a Q-Q plot from my data. Plot sample quantiles against normal (or specified) theoretical quantiles, add a 45-degree reference line fitted through the first and third quartiles, shade the 95% confidence band, label the axes with the distribution name, and annotate extreme points."
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Python Code Example
Console Output
Figure saved: plotivy-quantile-quantile-plot.png
Common Use Cases
- 1Checking normality of regression residuals before applying F-tests
- 2Assessing whether environmental concentration data follows a log-normal distribution
- 3Comparing empirical return period quantiles against a fitted GEV distribution
- 4Validating simulation model output distributions against reference datasets
Pro Tips
Fit the reference line through the 25th and 75th percentile points rather than the first and last for robustness
Use a log scale for the axes when checking log-normality to linearize the expected pattern
Add KS-statistic and p-value as an annotation to complement the visual assessment
Generate multiple Q-Q plots against different candidate distributions to select the best fit
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.