Variance
Standard Deviation tells you how spread out the set of scores are with respects to the mean. It measures the variability of the data. A small standard deviation implies that the data is close to the mean/average (+ or - a small range); the larger the standard deviation the more dispersed the data is from the mean.
The standard deviation provides in indication of what proportion of the entire distribution of the sample falls within a certain distance from the mean or average for that sample. If your data falls on a normal (or bell shaped) distribution, a SD of 1 indicates that about 68% of your data points (scores or whatever else) fall within 1 point (plus or minus) of the average (mean) of the data, and 95% fall within 2 points.
The standard deviation (SD) is a measure of spread so small sd = small spread. So the above is true for any distribution, not just the Normal.
In general, you cannot. If the distribution can be assumed to be Gaussian [Normal] then you could use z-scores.
It is not. And that is because the mean deviation of ANY variable is 0 and you cannot divide by 0.
Variance
mean
Standard deviation
The standard deviation is defined as the square root of the variance, so the variance is the same as the squared standard deviation.
Given a set of n scores, the variance is sum of the squared deviation divided by n or n-1. We divide by n for the population and n-1 for the sample.
The mean of a distribution of scores is the average.
T-scores have a mean of 50 and a standard deviation of 10. These values are fixed and do not change regardless of the distribution of T-scores.
It is 68.3%
Assuming a normal distribution 68 % of the data samples will be with 1 standard deviation of the mean.
Yes. It will increase the standard deviation. You are increasing the number of events that are further away from the mean, and the standard deviation is a measure of how far away the events are from the mean.
If the standard deviation of 10 scores is zero, then all scores are the same.
Quartile Deviation (QD)The quartile deviation is half the difference between the upper and lower quartiles in a distribution. It is a measure of the spread through the middle half of a distribution. It can be useful because it is not influenced by extremely high or extremely low scores. Quartile Deviation is an ordinal statistic and is most often used in conjunction with the median.why we calculating quartile deviation?