A low mean deviation (also called low mean absolute deviation) simply means that the data points are very close to the average (mean).
📊 In simple terms:
Mean deviation measures how far values are, on average, from the mean.
If it is low, then:
The values are clustered tightly around the mean
There is less variability or spread
The data is more consistent
🧠 Example:
Dataset A: 10, 11, 9, 10 → low mean deviation (values are close to 10)
Dataset B: 2, 10, 18 → high mean deviation (values are spread out)
✅ What it indicates:
High stability or uniformity
Mean is a good representative of the data
Less fluctuation
The mean of the sample means remains the same as the population mean, which is 128. The standard deviation of the sample means, also known as the standard error, is calculated by dividing the population standard deviation by the square root of the sample size. Therefore, the standard error is ( \frac{22}{\sqrt{36}} = \frac{22}{6} \approx 3.67 ). Thus, the mean is 128 and the standard deviation of the sample means is approximately 3.67.
None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0
It means that the observed value is greater than that which might be expected under the model being used. Often, it is deviation from the [arithmetic] mean.
No. The mean deviation is 0. Always.
The mean average deviation is the same as the mean deviation (or the average deviation) and they are, by definition, 0.
It means that the observations are all close to their mean value.
A negative deviation means that the observation is smaller than whatever it is that the deviation is being measured from.
What is mean deviation and why is quartile deviation better than mean deviation?
it means bobo pooped
Standard error of the mean (SEM) and standard deviation of the mean is the same thing. However, standard deviation is not the same as the SEM. To obtain SEM from the standard deviation, divide the standard deviation by the square root of the sample size.
Lower
The mean of the sample means remains the same as the population mean, which is 128. The standard deviation of the sample means, also known as the standard error, is calculated by dividing the population standard deviation by the square root of the sample size. Therefore, the standard error is ( \frac{22}{\sqrt{36}} = \frac{22}{6} \approx 3.67 ). Thus, the mean is 128 and the standard deviation of the sample means is approximately 3.67.
None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0
It means that the observed value is greater than that which might be expected under the model being used. Often, it is deviation from the [arithmetic] mean.
Standard deviation shows how much variation there is from the "average" (mean). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.
Large means something or an object that is big in size.
The 'standard deviation' in statistics or probability is a measure of how spread out the numbers are. It mathematical terms, it is the square root of the mean of the squared deviations of all the numbers in the data set from the mean of that set. It is approximately equal to the average deviation from the mean. If you have a set of values with low standard deviation, it means that in general, most of the values are close to the mean. A high standard deviation means that the values in general, differ a lot from the mean. The variance is the standard deviation squared. That is to say, the standard deviation is the square root of the variance. To calculate the variance, we simply take each number in the set and subtract it from the mean. Next square that value and do the same for each number in the set. Lastly, take the mean of all the squares. The mean of the squared deviation from the mean is the variance. The square root of the variance is the standard deviation. If you take the following data series for example, the mean for all of them is '3'. 3, 3, 3, 3, 3, 3 all the values are 3, they're the same as the mean. The standard deviation is zero. This is because the difference from the mean is zero in each case, and after squaring and then taking the mean, the variance is zero. Last, the square root of zero is zero so the standard deviation is zero. Of note is that since you are squaring the deviations from the mean, the variance and hence the standard deviation can never be negative. 1, 3, 3, 3, 3, 5 - most of the values are the same as the mean. This has a low standard deviation. In this case, the standard deviation is very small since most of the difference from the mean are small. 1, 1, 1, 5, 5, 5 - all the values are two higher or two lower than the mean. This series has the highest standard deviation.