There are few differences between variance and absolute mean deviation, but we will first understand each term.
Variance
Variance measures the variability or dispersion of data from the central tendency such as mean. Moreover, while seeing the variance, one could quickly tell about the spread of the data of a variable. And variance is calculated as an average squared distance between all the data points from the mean.
Absolute Mean Deviation
Absolute mean deviation also measures the dispersion of data from the central tendency such as median, and it calculates the absolute mean deviation as median of absolute difference from the median.
Major problem with Variance
In the presence of outliers, the mean suffers, and as a result, it produces a wrong statistic or parameters. However, the median does affect. For that reason, if there are outliers in the dataset, we mostly prefer absolute mean (by default median) deviation compared to variance.
At the same time, we must consider that the variance has its advantages and has many excellent statistical properties. The variance can be easily operated mathematically and algebraically than the absolute function like mean absolute deviation.
References
- Descriptive Statistics – Measures of Variability based on absolute deviation
- Descriptive Statistics – Measures of Variability based on squared deviation
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