A large standard deviation means that the data were spread out. It is relative whether or not you consider a standard deviation to be "large" or not, but a larger standard deviation always means that the data is more spread out than a smaller one. For example, if the mean was 60, and the standard deviation was 1, then this is a small standard deviation. The data is not spread out and a score of 74 or 43 would be highly unlikely, almost impossible. However, if the mean was 60 and the standard deviation was 20, then this would be a large standard deviation. The data is spread out more and a score of 74 or 43 wouldn't be odd or unusual at all.
If I have understood the question correctly, despite your challenging spelling, the standard deviation is the square root of the average of the squared deviations while the mean absolute deviation is the average of the deviation. One consequence of this difference is that a large deviation affects the standard deviation more than it affects the mean absolute deviation.
Standard deviation can be calculated using non-normal data, but isn't advised. You'll get abnormal results as the data isn't properly sorted, and the standard deviation will have a large window of accuracy.
It means that the data are spread out around their central value.
A sample of size 100.
No. A small standard deviation with a large mean will yield points further from the mean than a large standard deviation of a small mean. Standard deviation is best thought of as spread or dispersion.
A large standard deviation means that the data were spread out. It is relative whether or not you consider a standard deviation to be "large" or not, but a larger standard deviation always means that the data is more spread out than a smaller one. For example, if the mean was 60, and the standard deviation was 1, then this is a small standard deviation. The data is not spread out and a score of 74 or 43 would be highly unlikely, almost impossible. However, if the mean was 60 and the standard deviation was 20, then this would be a large standard deviation. The data is spread out more and a score of 74 or 43 wouldn't be odd or unusual at all.
Not necessarily. The equilibrium constant (K) quantifies the extent of a reaction at equilibrium, but it does not directly correlate to the rate of reaction. A large equilibrium constant indicates that the reaction favors the products at equilibrium, but the rate of the reaction depends on factors such as concentration, temperature, and catalysts.
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.
If I have understood the question correctly, despite your challenging spelling, the standard deviation is the square root of the average of the squared deviations while the mean absolute deviation is the average of the deviation. One consequence of this difference is that a large deviation affects the standard deviation more than it affects the mean absolute deviation.
That there is quite a large amount of variation between the observations.
A standard deviation in statistics is the amount at which a large number of given values in a set might deviate from the average. A percentile deviation represents this deviation as a percentage of the range.
Standard deviation can be calculated using non-normal data, but isn't advised. You'll get abnormal results as the data isn't properly sorted, and the standard deviation will have a large window of accuracy.
that you have a large variance in the population and/or your sample size is too small
It tells whether products or reactants are favored at equilibrium
A large equilibrium constant (Kc) indicates that the reaction favors the formation of products at equilibrium. This suggests that the reaction is proceeding almost to completion in the forward direction.
A small sample and a large standard deviation