Econ 310 test 3

A sample of size n is selected at random from an infinite population. As n increases, the standard error of the sample mean increases.

False

A specific confidence interval obtained from data will always correctly estimate the population parameter.

False

It is possible to commit a Type I error and a Type II error at the same time.

False

Reducing the probability of a Type I error also reduces the probability of a Type II error.

False

The Central Limit Theorem states that, if a random sample of size n is drawn from a population, then the sampling distribution of the sample mean :

Is approximately normal if n>30

We cannot commit a Type I error when the:

Null hypothesis is false

If the population distribution is skewed, in most cases the sampling distribution of the sample mean can be approximated by the normal distribution if the samples contain at least 30 observations. True False

True

If the population distribution is unknown, in most cases the sampling distribution of the mean can be approximated by the normal distribution if the samples contain at least 30 observations. True False

True

If there are two unbiased estimators of a parameter, the one whose variance is smaller is said to be relatively efficient. True False

True

In order to determine the p-value associated with hypothesis testing about the population mean μ, it is necessary to know the value of the test statistic.

True

Knowing that an estimator is unbiased only assures us that its expected value equals the parameter, but it does not tell us how close the estimator is to the parameter.

True

The amount of time it takes to complete a final examination is negatively skewed distribution with a mean of 70 minutes and a standard deviation of 8 minutes. If 64 students were randomly sampled, the probability that the sample mean of the sampled students exceeds 73.5 minutes is approximately 0.

True

An unbiased estimator of a population parameter is defined as:

an estimator whose expected value is equal to the parameter.

The problem with relying on a point estimate of a population parameter is that:

it is virtually certain to be wrong, it doesn't have the capacity to reflect the effects of larger sample sizes, it doesn't tell us how close or far the point estimate might be from the parameter.

Sampling distributions describe the distributions of:

sample statistics

An estimator is said to be consistent if:

the difference between the estimator and the population parameter grows smaller as the sample size grows larger