Class Width Calculator
Calculate optimal class width for histograms and frequency distributions
Calculate Class Width
Highest value in your dataset
Lowest value in your dataset
Desired number of intervals
Class Width Results
Class Width Analysis
Example: Student Test Scores
Test Score Data
Scores: 45, 68, 82, 79, 67, 55, 75, 55, 85, 89, 90, 78, 45, 66, 49
Maximum score: 90
Minimum score: 45
Desired classes: 9
Calculation Steps
1. Range = 90 - 45 = 45
2. Class Width = 45 ÷ 9 = 5
3. Final Class Width = 5
4. Classes: 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89
Class Width Guidelines
Choose Classes
5-20 classes work best
Too few = oversimplified, too many = cluttered
Calculate Width
Width = Range ÷ Classes
Round up to convenient numbers
Create Intervals
Use equal-width intervals
Ensure no gaps or overlaps
Quick Tips
Round class width to convenient numbers (5, 10, 25, etc.)
All class intervals should have the same width
Choose class boundaries that don't split data points
Consider your audience and the purpose of the histogram
Understanding Class Width in Statistics
What is Class Width?
Class width is the difference between the upper and lower boundaries of any class interval in a frequency distribution. It represents the range of values that each class covers and is essential for creating meaningful histograms and frequency distributions.
Why is Class Width Important?
- •Creates clear visual representation of data distribution
- •Helps identify patterns and trends in datasets
- •Essential for creating accurate histograms
- •Facilitates data analysis and interpretation
Formula and Calculation
Class Width = (Maximum - Minimum) ÷ Number of Classes
- Maximum: Highest value in the dataset
- Minimum: Lowest value in the dataset
- Range: Maximum - Minimum
- Number of Classes: Desired number of intervals
Tip: Round the calculated class width up to the nearest convenient number for easier interpretation and plotting.
Choosing the Right Number of Classes
Too Few Classes (<5)
Oversimplifies data, loses important details, may miss distribution patterns
Optimal Range (5-20)
Balances detail with clarity, reveals distribution shape, easy to interpret
Too Many Classes (>20)
Creates clutter, may hide patterns, difficult to interpret visually