The video tutorial explores whether the sample median is an unbiased estimator of the population median. Alejandro conducts an experiment using ping-pong balls to create a sampling distribution. The video explains the concept of unbiased estimators and how the sampling distribution should be balanced around the true population parameter. It also includes an example with dot plots to analyze different estimators' biases and variability.

What was Alejandro trying to determine with his experiment involving ping-pong balls?

• If the sample range is an unbiased estimator of the population range

• If the sample mode is an unbiased estimator of the population mode

• If the sample median is an unbiased estimator of the population median

• If the sample mean is an unbiased estimator of the population mean

How many trials did Alejandro conduct in his experiment?

• 10 trials

• 20 trials

• 50 trials

• 100 trials

What is required for an estimator to be considered unbiased?

• The sampling distribution should be skewed to the right

• The sampling distribution should be skewed to the left

• The sampling distribution should be evenly distributed around the true parameter

• The sampling distribution should have a high variance

What does a balanced sampling distribution indicate about an estimator?

• The estimator is inaccurate

• The estimator has high variability

• The estimator is unbiased

• The estimator is biased

In the context of the video, what does it mean if a sampling distribution is consistently to the left of the true parameter?

• The estimator is unbiased

• The estimator is overestimating the true parameter

• The estimator has high variability

• The estimator is underestimating the true parameter

What is the population parameter value in the dot plot example?

• 7

• 3

• 5

• 9

Which statistic in the dot plot example is clearly biased?

• Statistic A

• Statistic B

• Statistic C

• None of the statistics

What characteristic does Statistic A have in the dot plot example?

• High bias and low variability

• High bias and high variability

• Low bias and high variability

• Low bias and low variability

Why is low variability important in an estimator?

• It indicates the estimator is always accurate

• It shows the estimator is consistent around the true parameter

• It means the estimator is biased

• It suggests the estimator is unreliable

What does it mean if a statistic has low bias?

• It has a wide spread around the true parameter

• It is close to the true parameter on average

• It is consistently underestimating the true parameter

• It is consistently overestimating the true parameter