The question asks which of the following is a biased estimator, meaning which does not accurately target the population parameter.

Here's an analysis of the options:

1. Median - The sample median is typically an unbiased estimator of the population median.
2. Proportion - The sample proportion is an unbiased estimator of the population proportion.
3. Variance - The sample variance (calculated as $\frac{1}{n-1} \sum (x_i - \bar{x})^2$) is an unbiased estimator of the population variance. However, if calculated using $\frac{1}{n} \sum (x_i - \bar{x})^2$, it becomes biased.
4. Mean - The sample mean is an unbiased estimator of the population mean.

Answer: The correct answer is Variance, when it is calculated using $\frac{1}{n}$, as this does not accurately target the population variance and is considered a biased estimator.

Would you like more details or have any further questions? Here are some related questions you could explore:

1. Why does dividing by $n - 1$ rather than $n$ give an unbiased variance estimate?
2. What are other common examples of biased and unbiased estimators?
3. How do you calculate the sample mean and population mean?
4. What is the significance of biased vs. unbiased estimators in statistical inference?
5. How does sample size affect the accuracy of different estimators?

Tip: Remember, an unbiased estimator has an expected value equal to the parameter it estimates.