Describe your mean, spread, and any shaded regions, and Figviz will render a clean, accurate normal distribution diagram on the spot. Built for researchers, instructors, and analysts who need publication-quality visuals without wrestling with graphing software.
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The canonical Z-distribution with mean=0 and σ=1, illustrating the three-sigma rule with color-coded bands showing 68%, 95%, and 99.7% coverage.
A grade-boundary overlay on a normal distribution, showing what share of a class lands in each letter-grade range when scores average 75.
Side-by-side normal distributions commonly used to visualize treatment effects and group differences in hypothesis testing.
A two-tailed hypothesis test layout marking the 95% confidence band and the alpha=0.05 rejection regions at the distribution tails.
An IQ score distribution chart with population-percentage breakdowns across cognitive classification ranges.
A Six Sigma process capability diagram showing how closely a production process clusters around its target value relative to specification limits.
Start with one of these examples, then adapt the subject, labels, data, or layout for your own use.
The canonical Z-distribution with mean=0 and σ=1, illustrating the three-sigma rule with color-coded bands showing 68%, 95%, and 99.7% coverage.
Draw a standard normal distribution centered at 0. Place vertical markers at every whole standard deviation from -3σ to +3σ. Use progressively lighter blue shading for the ±1σ (68%), ±2σ (95%), and ±3σ (99.7%) bands and label each percentage. Academic publication style, white background.
A grade-boundary overlay on a normal distribution, showing what share of a class lands in each letter-grade range when scores average 75.
Draw a bell curve for a class exam where the mean score is 75 and the standard deviation is 10. Plot the x-axis from 45 to 105. Mark grade cutoffs: F below 60, D from 60-70, C from 70-80, B from 80-90, A from 90-100. Fill each band with a distinct color and label the percentage of students it contains. Academic style, white background.
Side-by-side normal distributions commonly used to visualize treatment effects and group differences in hypothesis testing.
Plot two overlapping bell curves on the same axes. Control group in blue: mean=50, σ=8. Treatment group in red: mean=58, σ=8. Shade the overlap area in purple. Mark each mean with a vertical dashed line and add a legend. Title the chart "Treatment Effect Comparison". Clean medical-research style, white background.
A two-tailed hypothesis test layout marking the 95% confidence band and the alpha=0.05 rejection regions at the distribution tails.
Draw a standard normal curve highlighting a 95% confidence interval. Shade the central 95% region in light blue. Mark the critical values at z=-1.96 and z=+1.96 with vertical dashed lines. Color both tail rejection zones (2.5% each) in red. Label the confidence level, alpha, and critical values clearly. Statistics textbook style, white background.
The bell curve, formally called the normal distribution, describes how measurements from natural and social phenomena cluster. Its shape is symmetrical: most observations sit near the center, and counts taper toward the extremes. The highest point marks the average, while the width of the spread is captured by a single number, the standard deviation. Understanding those two values lets you read any normally distributed dataset at a glance, and Figviz makes it equally fast to put that picture on a slide or paper.
Plotting an accurate bell curve by hand means computing probability density values, scaling axes, and applying consistent styling, none of which adds insight. Figviz handles the arithmetic and layout so you can focus on what the shape means. Describe the scenario in plain language, choose a visual style, and the generator produces a correctly proportioned diagram whether you are mapping standardized test results, charting a manufacturing tolerance window, or teaching the central limit theorem in a classroom.
Not every dataset follows a symmetric, single-peaked distribution. Income figures often skew right because a small group of very high earners pull the tail outward. Reaction times can pile up near a lower floor. Before relying on a normal distribution diagram to represent your data, confirm the underlying shape with a histogram or a normality test. Figviz is optimized for true normal distributions; knowing where the model fits keeps your analysis honest.
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