Data input
Enter numbers separated by commas or spaces, or use the table. Upload a .csv file (one row of numbers or comma-separated values).
Or edit as table
Central value for deviation
Rounding
Formulas
Grouped data (value × frequency)
Each row is a distinct value and how often it occurs.
Uses the same central value and rounding options as the Ungrouped tab.
Compare two datasets
Comma-separated numbers for each set. Compare mean absolute deviation about the mean (typical for cross-dataset comparison).
Exam practice
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Find mean deviation from the mean (rounded to 2 decimals).
What is mean deviation?
Mean deviation (or mean absolute deviation) is the average distance of data points from a chosen central value — usually the mean, median, or mode. It describes dispersion in the same units as your data.
Mean vs standard deviation
Standard deviation squares deviations before averaging, so it is more sensitive to outliers. Mean deviation uses absolute values, stays interpretable in original units, and can use median or mode when you want a robust center.
When to use mean deviation?
Use it in introductory statistics, exam problems, and when you need a simple “average gap” from a center. For grouped exam data, frequency-weighted formulas match textbook methods.
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FAQ
- What is the mean deviation formula?
- MD = (1/n) Σ |xi − C|, where C is the mean, median, or mode depending on your method. For grouped data, MD = Σ fi|xi − C| / Σ fi.
- How do you calculate mean deviation?
- Pick C (mean, median, or mode). For each observation, subtract C and take the absolute value. Add those values and divide by n (or total frequency).
- What is the difference between variance and deviation?
- Variance uses squared differences from the mean. Mean deviation uses absolute differences and can use other centers — they measure spread differently.
- Why use mean deviation?
- It is easy to explain, matches many textbook exercises, and stays in the same units as the data.