Answer:
The correct answer is All of the answers above correctly describe the standard deviation.
Step-by-step explanation:
From the information given:
"The standard deviation is the square root of the corresponding variance" this statement is true because this is the way is defined [tex]\sigma = \sqrt {\mu _2 }[/tex] where [tex]\mu _2[/tex] is the variance.
The statement "The standard deviation is the average amount that scores in a distribution deviate from the mean of that distribution." is true because it measures the spread of data about the mean value and this is the way is defined [tex]\sigma = \sqrt{\frac{\sum{(x-\mu)^2}}{N}} \nonumber [/tex] where [tex]\mu[/tex] is the mean, x denotes each value of data and N is the set of values.
"The standard deviation is a measure of variability." is true because the standard deviation measures the spread of a data distribution and it is a useful measure of variability when the distribution is normal or approximately normal.
Therefore the correct answer is All of the answers above correctly describe the standard deviation.