Correlation does not prove agreement. Bland-Altman plots show the bias between two measurement methods, the spread of differences, and the limits of agreement that matter in clinical and laboratory validation studies.
If you need to validate a new assay, compare two instruments, or check whether a revised workflow produces the same result as an established method, Bland-Altman analysis is the right plot. It makes systematic bias and outlier spread visible in a way a simple scatter plot cannot.
Compare legacy and updated devices on the same measurement scale.
Check whether two lab methods agree within an acceptable tolerance.
Show bias, spread, and repeatability for biomarker measurements.
Identify whether a method is consistently higher or lower than the reference.
Plot the mean difference directly so readers can see whether one method systematically runs high or low.
Draw the standard 95% bounds to reveal whether the observed spread is acceptable for the use case.
Use a clean manuscript layout for instrument qualification and method comparison documents.
Keep the code editable so reruns on new validation batches produce the same visual standard.
This example compares two methods across paired observations, computes the mean bias and 95% limits of agreement, and fills the agreement band for a cleaner publication figure.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Complementary chart templates for validation and method comparison
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 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
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))
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],Turn paired measurements into a clear method-comparison figure that shows bias, spread, and whether your new method stays within the right tolerance.