Mean Absolute Deviation Calculator

Calculate MAD to measure the average distance from a central point

Calculate Mean Absolute Deviation

0 valid values entered

Enter numerical values one by one. New fields will appear automatically up to 50 values.

Enter Some Data Values

Please enter at least one numerical value to calculate the Mean Absolute Deviation.

Example: Lost Socks Analysis

Data: Lost Socks per Person

6 friends lost these many socks last year: 3, 17, 9, 7, 13, 11

Mean: (3 + 17 + 9 + 7 + 13 + 11) ÷ 6 = 10 socks

MAD Calculation Steps

1. Deviations from mean (10): -7, 7, -1, -3, 3, 1

2. Absolute deviations: 7, 7, 1, 3, 3, 1

3. Sum of absolute deviations: 7 + 7 + 1 + 3 + 3 + 1 = 22

4. MAD: 22 ÷ 6 = 3.67 socks

Interpretation

On average, each person's sock loss differs from the mean by 3.67 socks. This means most friends lost between 6.33 and 13.67 socks (10 ± 3.67).

Central Point Options

📊
Mean
Average of all data points
📈
Median
Middle value when sorted
🎯
Custom
Any value you specify

MAD Interpretation

Low MAD
Data clustered near center
Moderate MAD
Moderate spread
High MAD
Wide dispersion

Quick Tips

MAD measures average distance from central point

Lower MAD means data is more consistent

MAD is less sensitive to outliers than standard deviation

Use custom central point for specific analysis needs

Understanding Mean Absolute Deviation (MAD)

What is MAD?

Mean Absolute Deviation (MAD) measures how spread out data points are from a central value. It calculates the average of the absolute differences between each data point and the central point, giving you a clear picture of data variability.

Why Use MAD?

  • Easy to understand and interpret
  • Less sensitive to outliers than standard deviation
  • Measures dispersion in original units
  • Useful for robust statistical analysis

MAD Formula

MAD = (1/n) × Σ|xi - m|
  • MAD: Mean Absolute Deviation
  • n: Number of data points
  • xi: Each individual data point
  • m: Central point (mean, median, or custom)
  • |xi - m|: Absolute deviation of each point

Key Insight: MAD represents the average distance that data points deviate from the central value, making it intuitive to interpret.