BStat2

After computing a confidence interval, the user believes the results are meaningless because the width of the interval is too large. Which one of the following is the best recommendation?

Increase the sample size

What type of error occurs if you fail to reject H0 when, in fact, it is not true?

Type II

The error of rejecting a true null hypothesis is

a Type I error

If a hypothesis test leads to the rejection of the null hypothesis,

a Type I error may have been committed

The p-value is

a probability

A probability distribution for all possible values of a sample statistic is known as

a sampling distribution

A normal distribution with a mean of 0 and a standard deviation of 1 is called

a standard normal distribution

A continuous random variable may assume

all values in an interval or collection of intervals

Whenever the population has a normal probability distribution, the sampling distribution of is a normal probability distribution for

any sample size

The z score for the standard normal distribution

can be either negative or positive

A theorem that allows us to use the normal probability distribution to approximate the sampling distribution of sample means and sample proportions whenever the sample size is large is known as the

central limit theorem

The ability of an interval estimate to contain the value of the population parameter is described by the

confidence level

If the standard deviation of an estimator decreases with larger sample sizes, then the estimator is

consistent

As the sample size increases, the margin of error

decreases

If the standard deviation of an estimator is the smallest among all other unbiased estimators, then the estimator is

efficient

The sample mean has the smallest variance among all unbiased estimators of the population mean. Therefore, the sample mean is

efficient

As the test statistic becomes larger, the p-value

gets smaller

The mean of a standard normal probability distribution

is always equal to 0

The center of a normal curve is

is the mean of the distribution

The value added and subtracted from a point estimate in order to develop an interval estimate of the population parameter is known as the

margin of error

The level of significance is the

maximum allowable probability of Type I error

As the sample size becomes larger, the sampling distribution of the sample mean approaches a

normal distribution

Parameters

numerical characteristics of a population

The purpose of statistical inference is to provide information about the

population based upon information contained in the sample

An interval estimate is a range of values used to estimate

population parameter

The level of significance in hypothesis testing is the probability of

rejecting a true null hypothesis

The probability distribution of the sample mean is called the

sampling distribution of the mean

The standard deviation of all possible values is called the

standard error of the mean

When s is used to estimate , the margin of error is computed by using

t distribution

Whenever the population standard deviation is unknown and the population has a normal or near-normal distribution, which distribution is used in developing an interval estimation?

t distribution

The probability of committing a Type I error when the null hypothesis is true is

the Level of Significance

In the hypothesis testing procedure, "alpha" is

the level of significance

The highest point of a normal curve occurs at

the mean

In determining the sample size necessary to estimate a population proportion, which of the following information is not needed?

the mean of the population

The p-value is a probability that measures the support (or lack of support) for the

the null hypothesis

A negative value of Z indicates that

the number of standard deviations of an observation is to the left of the mean

In interval estimation, the t distribution is applicable only when

the sample standard deviation is used to estimate the population standard deviation

If we change a 95% confidence interval estimate to a 99% confidence interval estimate, we can expect

the size of the confidence interval to increase

If the expected value of an estimator equals the parameter its estimating, then the estimator is

unbiased

Larger values of the standard deviation result in a normal curve that is

wider and flatter

In general, higher confidence levels provide

wider confidence intervals

The probability of making a Type I error is denoted by

"alpha"