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Course: Measurement & Data - Statistics & Probability 218-221>Unit 1

Lesson 8: Mean absolute deviation (MAD)

Mean absolute deviation (MAD) of a data set is the average distance between each data value and the mean. Mean absolute deviation is a way to describe variation in a data set. Mean absolute deviation helps us get a sense of how "spread out" the values in a data set are.

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• I still don't get how to find the MAD, can anyone pls help me
1. finding the mean(average) of the set of numbers
2. find the distance of all the numbers from the mean.
3. Find the mean of those numbers.
• Wait, so we have to find the mean and then the absolute value right?
• You need to find the average, then find the distance of each data point from your average, then average that to get MAD.
• Is there an easier way to calculate MAD? So much writing!
• Well, we can solve the writing problem by doing mental math, but we can't solve the easy way part.
• Is this different from standard deviation? I find that I get different answers from both, but they seem like the same concept. Can you please explain the difference and purpose of each?
• The difference between this and standard deviation is that, in standard deviation you are squaring the sum of all the numbers that deviate from the mean; in MAD you don't (you simply divide the sum/# in sample)
• but how do you do these things and not get them wrong:{
• There are a lot of calculations and it's easy to get one wrong.
Be patient, take your time, and never assume you got it right on your first try.
• please don't judge me but is the mean the same as the avrege?
• Yes, mean = average
• I am so confused. Can someone explain how to find the MAD?
• How do you do this
• To calculate Mean Absolute Deviation:

Find the mean of the data set.
For each data point, subtract the mean and take the absolute value to find the absolute deviation.
Sum up all the absolute deviations.
Divide the sum of absolute deviations by the total number of data points to find the MAD.
• There was a distinction made between a sample variance/standard deviation and a population variance/standard deviation. The population variance is calculated by taking the sum of the squared deviations from each data point to the population mean, and then dividing by the number of data points in the population. On the other hand, the sample variance goes through the same process as above, except it's with respect to the sample mean, and you should also divide by one less than the number of data points in your sample, to correct the bias (Bessel's Correction). I'm wondering if a similar notion exists for the Mean Absolute Deviation (MAD)? In other words, whether it's a sample or population we're dealing with, is there any significant difference in the way that the MAD is calculated for either of them?