Variance is the average squared deviations from the mean, while standard deviation is the square root of this number. Both measures reflect variability in a distribution, but their units differ: · Standard deviation is expressed in the same units as the original values (e.g., minutes or meters). · Variance is expressed in much larger units (e.g., meters squared). · Although the units of variance are harder to intuitively understand, variance is important in statistical tests.

The standard deviation is the average amount of variability in your data set. It tells you, on average, how far each score lies from the mean. · In normal distributions, a high standard deviation means that values are generally far from the mean, while a low standard deviation indicates that values are clustered close to the mean.

In a normal distribution, data are symmetrically distributed with no skew. Most values cluster around a central region, with values tapering off as they go further away from the center. · The measures of central tendency (mean, mode, and median) are exactly the same in a normal distribution.