Mean Absolute Deviation Calculator

Mean Absolute Deviation (MAD) is a measure of variability that shows the average distance between each data point and the mean. Unlike variance, MAD uses absolute values instead of squared differences, making it more intuitive to interpret. It's useful for understanding how spread out your data is from the average.

MAD is calculated by: 1) Finding the mean of all values, 2) Subtracting the mean from each value and taking the absolute value, 3) Finding the average of these absolute differences. Formula: MAD = Σ|x - x̄| / n, where x̄ is the mean and n is the number of data points.

Both measure variability, but MAD uses absolute differences while standard deviation uses squared differences. MAD is more robust to outliers because it doesn't square the differences, making extreme values less influential. Standard deviation is more commonly used in statistical tests, but MAD is easier to interpret.

Use MAD when you want a measure of variability that's less sensitive to outliers, easier to interpret, or when you need to explain the concept to non-technical audiences. MAD is also useful in robust statistics and when you want to avoid the influence of extreme values.

No, MAD cannot be negative. Since MAD involves taking absolute values of differences from the mean, all terms in the calculation are non-negative, making the final result always positive or zero. Zero MAD means all values are identical.

High MAD indicates that data points are widely spread out from the mean, suggesting high variability or dispersion. This could mean the data is diverse, has outliers, or comes from a population with high variability. Low MAD suggests data points are clustered close to the mean.

The absolute deviations show how far each data point is from the mean, without considering direction (positive or negative). Larger values indicate points further from the mean. These values are used to calculate MAD and help you see which data points contribute most to the overall variability.

Both MAD and range measure spread, but differently. Range is simply max - min, while MAD considers all data points and their distances from the mean. MAD is more robust because it's not affected by just two extreme values like range is, and it provides a more representative measure of typical deviation.

Use 2-4 decimal places for MAD, depending on your data precision and context. For most practical purposes, 2-3 decimal places are sufficient. The calculator shows up to 2 decimal places but you can round as needed for your specific use case.

MAD is used in quality control (measuring product consistency), finance (risk assessment), education (analyzing test scores), weather forecasting (temperature variability), and many other fields where understanding data spread is important and you want a measure that's less sensitive to outliers.

MAD is more robust than variance/standard deviation because it's less affected by outliers. It's easier to interpret since it's in the same units as your data. However, it's less commonly used in statistical tests because many statistical methods are based on squared differences (variance).

Yes, MAD can be used to compare variability between datasets, but be cautious when the means are very different. For datasets with similar means, MAD provides a good comparison. For datasets with very different means, you might want to use the coefficient of variation (MAD/mean) for a relative comparison.