Histogram & Distribution Maker
Create histograms from your data, analyze distributions, and visualize frequency patterns
Histogram Visualization
Enter data to generate histogram
Understanding Histograms
A histogram is a graphical representation of data distribution. It groups data into bins (intervals) and displays the frequency of values in each bin as bars.
Key Concepts
- Bins (Class Intervals): Ranges of values that data is grouped into. Too few bins oversimplify; too many create noise.
- Frequency: The count of data points that fall within each bin.
- Bin Width: The size of each interval. Consistent widths make patterns easier to see.
- Distribution Shape: Histograms reveal whether data is symmetric, skewed, bimodal, or uniform.
Common Bin Rules
- Sturges' Rule: k = ⌈log₂(n)⌉ + 1, good for normally distributed data
- Scott's Rule: Bin width = 3.5σ / n^(1/3), minimizes integrated mean squared error
- Freedman-Diaconis: Bin width = 2×IQR / n^(1/3), robust against outliers
Distribution Shapes
📊 Normal/Symmetric
Bell-shaped curve with values concentrated around the mean
Example: Heights, test scores, measurement errors
📈 Skewed
Tail extends more to one side (left-skewed or right-skewed)
Example: Income distribution (right-skewed), age at retirement (left-skewed)
🎯 Bimodal
Two distinct peaks, indicating two separate groups
Example: Customer ages (young adults + seniors), test scores (pass/fail groups)
Pro Tip: Try adjusting the number of bins to see how the distribution shape changes. Different bin sizes can reveal or hide patterns in your data!
When to Use Histograms
- Understanding the distribution of continuous numerical data
- Identifying outliers and unusual patterns
- Checking if data follows a normal distribution
- Comparing distributions between different groups
- Finding central tendency and spread visually