QQ plots are the quickest way to compare sample quantiles against a theoretical distribution. Use them to inspect normality, validate model residuals, and reveal skew or heavy tails before you overfit the next analysis.
A QQ plot is a diagnostic figure, not just a statistical ornament. It shows whether your data actually follow the assumptions behind a t-test, linear model, or other parametric method. If the points bend away from the reference line, your data are telling you something useful about skew, outliers, or non-normal tails.
Verify whether a sample looks reasonably Gaussian before using parametric tests.
Inspect model errors after regression, ANOVA, or curve fitting.
Spot heavy tails, skew, or truncation that a histogram can hide.
Compare transformed data against the target theoretical distribution.
Turn one visual into a clear decision point before moving to inferential statistics.
Use a clean reference line, legible axes, and a minimal layout that reads well in a methods section.
Make it obvious when data need transformation, trimming, or a non-parametric approach.
Keep the exact diagnostic logic transparent for review, collaboration, and reruns.
This example uses a slightly skewed sample so the departure from the reference line is easy to see. Replace the values with your own residuals or measurement series to validate a real model.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Diagnostic and summary charts that help with distribution analysis
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Displays the distribution of numerical data by grouping values into bins.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm
# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80) # Clip to realistic range
fig, ax = plt.subplots(figsize=(12, 7))
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced styling
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30
Combines box plots with kernel density to show distribution shape across groups.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway
# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 50)Use a QQ plot to decide whether your data are close enough to normal, whether a transform is needed, or whether a different analysis path makes more sense.