The option that represents the conditions that will create a biased estimator of a population parameter is;
D: The expected value of the estimator is not equal to the population parameter.
<h3>Biased and Unbiased Estimators</h3>
Usually in statistics research, there is what we call population parameters and sampling parameters.
The population parameters represent the full pool from which a sample can be taken while the sample parameters are gotten from the sample taken from the population.
Now, a statistic will be referred to as an unbiased estimator of a population parameter if the mean of the sampling distribution of the statistic is equal to the value of the population parameter.
In contrast to an unbiased estimator, a biased estimator of a population parameter is referred to as such if the mean of the sampling distribution of the statistic is not equal to the value of the population parameter.
The missing options are;
A. The sampling distribution of the estimator is skewed to the left.
B. The sampling distribution of the estimator is skewed to the right.
C. The sampling distribution of the estimator is not the same shape as the distribution of the population parameter.
D. The expected value of the estimator is not equal to the population parameter.
E. The variability of the sampling distribution of the estimator is not equal to the variability of the population parameter.
Read more about biased estimators at; brainly.com/question/7301139
