## Characteristics of Estimators

This section discusses two important characteristics used as point estimates of parameters: bias and sampling variability. Bias refers to whether an estimator tends to over or underestimate the parameter. Sampling variability refers to how much the estimate varies from sample to sample.

### Questions

Question 1 out of 4.
You are playing "Pin the Tail on the Donkey" at your friend's birthday party. While blindfolded, you have three tries to pin the tail in the correct location. All three times you pin it about a foot too low, and it lands on the donkey's back hooves. Select all of the terms that describe your location estimation.
unbiased
biased
variable
not variable

Question 2 out of 4.
In the population, a parameter has a value of 15. Based on the means and standard errors of their sampling distributions, which of these estimators shows the most bias?

Mean = 14, SE = 2
Mean = 8, SE = 2
Mean = 15, SE = 6
Mean = 20, SE = 1

Question 3 out of 4.
In the population, a parameter has a value of 10. Based on the means and standard errors of their sampling distributions, which of these statistics estimates this parameter with the least sampling variability?

Mean = 10, SE = 5
Mean = 9, SE = 4
Mean = 11, SE = 2
Mean = 13, SE = 3

Question 4 out of 4.
In a population, a parameter called "mobent" has a value of 9. If the statistic estimating mobent is unbiased, what is its expected value?