Mean Absolute Deviation Calculator (MAD)
The mean absolute deviation (MAD) is the average distance of each value from the centre. The tool reports the version around the mean (Excel's AVEDEV() and the Common Core textbook MAD) plus the version around the median, which is more robust to outliers.
MAD (around the mean)
1.5
MAD = (1 ⁄ n) · Σ |xᵢ − x̄|, the version returned by Excel AVEDEV() and the Common Core textbook definition.
MAD (around the median)
1.25
Uses the median as the centre — more robust to outliers and common in robust statistics and risk analytics.
Descriptive statistics
- Count n
- —
- Sum Σx
- —
- Mean x̄
- —
- Median
- —
Show working (per-value absolute deviations)
| i | xᵢ | |xᵢ − x̄| | |xᵢ − median| |
|---|
Formula
MAD = (1 ⁄ n) · Σ |xᵢ − x̄| (centre = arithmetic mean) MAD = (1 ⁄ n) · Σ |xᵢ − median| (centre = median — more robust)
- · Relationship with the standard deviation: for a normal distribution N(μ, σ²), MAD ≈ σ · √(2/π) ≈ 0.7979 σ; equivalently σ ≈ 1.2533 · MAD.
- · More intuitive units than σ: MAD keeps the original units (minutes, kg, dollars) — there is no squaring/square-root step, so it is easier to explain to students and non-statisticians.
- · More robust to outliers (especially the median-centred MAD): the variance squares each residual, so one extreme value can dominate σ — MAD just takes absolute values and softens the influence.
- · Common uses: (1) school statistics (Common Core 6.SP.B.5, GCSE Statistics); (2) forecast accuracy (the related Mean Absolute Error / MAE); (3) robust regression — minimising MAD ≡ L1 / LAD regression.
- · This tool uses 1/n (the population denominator), not 1/(n−1). Unlike σ, the MAD does not have a widely-adopted Bessel-style sample correction; textbooks use n.
- · References: Pham-Gia & Hung, "The Mean and Median Absolute Deviations", Math & Comp Modelling 34, 2001; Common Core State Standards 6.SP.B.5.c.
Frequently asked
How is MAD different from variance / standard deviation — which should I use?
Both measure spread, but they treat outliers differently. The standard deviation uses (xᵢ − x̄)² — squaring amplifies extreme values, making σ very sensitive to outliers. MAD uses |xᵢ − x̄| — only linear penalty, much gentler. Which to use? (1) If the distribution is roughly normal, prefer σ because most inferential machinery (z-scores, confidence intervals, t-tests) is built on it. (2) If the data has heavy tails or visible outliers (financial returns, earthquake magnitudes), use MAD or the more robust median-centred version. (3) For teaching and non-statistical audiences, MAD is easier to interpret because the units match the data. (4) For forecast accuracy, MAE (essentially MAD) is the industry standard — used across the M-competition forecasting series.
What's the difference between MAD around the median and the Median Absolute Deviation (MeAD)?
Both use the median as the centre, but they differ in the *outer* aggregation. This tool's "MAD around the median" is (1/n)·Σ|xᵢ − median| — the *mean* of absolute deviations; this is sometimes called the "Mean Absolute Deviation about the Median" in the literature. The Median Absolute Deviation (MeAD, also abbreviated MAD in robust-stats papers — confusingly different) is median(|xᵢ − median(x)|) — the *median* of the absolute deviations. MeAD has a higher breakdown point (50 %) and is the canonical scale estimator in robust statistics; multiplying by 1.4826 makes it consistent with σ under a normal model, which is why it shows up in robust regression and robust Z-score variants. So: MAD (around the median) here = mean of |x − median|; MeAD = median of |x − median|.
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