Exam 7: Understanding Data: Using Statistics for Inference and Estimation

A standard error of the mean is a standard deviation of a.

The estimated standard error of the mean, s X

Which of the following statements is true?

If each member of a population has an equal chance of being selected for a sample and the selection of one member of the population is independent of the selection of another member, then the method of sampling is sampling.

An interval in which we can have a specified confidence that the interval includes μ is called a(n).

Sampling error

If an estimator is consistent, more accurate estimates of population parameters will be obtained from samples that are.

If s = 10 and s X = 2, then N must be equal to.

A frequency distribution of sample means obtained by actually selecting samples from a population and then computing the means of those samples is called a(n) sampling distribution of the mean.

A theoretical probability distribution of values of a statistic resulting from selecting all possible samples of size N from a population is called a distribution of that statistic.

To obtain accurate estimates of a parameter, a sample should be.

A researcher can expect to obtain more accurate estimates of population parameters with random samples from populations in which the variability in scores to be sampled is .

The value of s X is affected by the size of the sample and the.

You can be about percent confident that an interval that includes the values between one standard error below the sample mean and one standard error above the sample mean will include the population mean.

A sampling distribution of the mean can be described in terms of its.

Suppose you have a statistic such that, as the sample size becomes larger, the probability that the statistic comes closer to the parameter it estimates does not increase. This statistic is estimator.

Which of the following considerations influences how well a statistic estimates a parameter?

As sample size increases, a sampling distribution of the mean will approximate a normal distribution, regardless of the shape of the distribution of scores in the population. This characteristic of the sampling distribution of the mean is stated in the.

As the sample size increases the probability increases that the population parameter is more closely estimated by the statistic; thus, the estimator is.

The the variation in the population and the the sample size, then the larger the estimated standard error.