stats test 2
When the sample size increases, everything else remaining the same, the width of a confidence interval for a population parameter will:
Decrease
As the margin of error decreases, the width of the confidence interval
Decreases
Confidence intervals always contain the true population value.
False
You have sampled 25 randomly selected students to find the mean test score. A 95% confidence interval for the mean came out to be between 85 and 92. Which of the following statements gives a valid interpretation of this interval?
If this procedure were to be repeated many times, 95% of the confidence intervals found would contain the true mean score.
If the standard deviation of a sample increases and you want the confidence interval width to remain the same, then the sample size must
Increase
In the construction of a confidence interval, as the confidence level required in estimating the mean increases, the width of the confidence interval
Increases
Which of the following is an advantage of confidence interval estimate over a point estimate for a population parameter?
Interval estimates take into account the fact that the statistic being used to estimate the population parameter is a random variable
When using the same data, the width of a confidence interval will be:
Narrower for 90% confidence than 95% confidence.
Which of the following are reasons that a sample may be a biased or unreliable sample. Choose all that apply.
People who were asked refused to answer. Trying to conclude that there is a cause-and-effect relationship when something else causes both. The funders of the project are partial to the results. The graphs are drawn in a way to mislead the reader.
Which of the following are reasons that a sample may be a biased or unreliable sample.
The sample is not representative of the population. Self-Selected Sample. (Voluntary Sample) The sample size is too small. The wording of survey question influences the response.
As the confidence level increases the margin of error will increase.
True
When the level of confidence and sample standard deviation remain the same, a confidence interval for a population mean based on a sample of n = 100 will be narrower than a confidence interval for a population mean based on a sample of n = 50.
True
The mean of the sampling distribution of means of all possible random samples size n is:
equal to the mean of the population.
A sampling distribution is made up of 1 sample.
false
As the confidence level decreases the confidence interval will get wider.
false
As the confidence level increases the confidence interval will get narrower
false
As the sample size increases the margin of error will increase.
false
As the sample size increases, the standard error gets larger
false
As the sample size increases, the standard error gets larger.
false
Calculating standard error of a sampling distribution is not important, we only need to look at the regular standard deviation of a single data set.
false
Confidence intervals always contain the population mean or proportion
false
It doesn't matter if the population is skewed, the distribution of sample means will always be bell shaped.
false
Random is not important in the central limit theorem.
false
Random samples should have at least 10 successes and at least 10 failures if we want our random sample percent to estimate the population percent. This means we will always need a data set size of exactly 20.
false
Random samples will be exactly the same as the population value.
false
Random samples will be guaranteed close to the population value.
false
The CLT only applies to sample means and does not apply to the distribution of sample percentages.
false
The average (center) of all the random sample percentages (proportions) will be a poor estimate of the population percent.
false
The sample mean from 1 sample is usually a more accurate estimate of the population mean than the center of a sampling distribution.
false
The sample percentage from just a single data set is usually much closer to the population percentage than the center of a sampling distribution of sample percentages.
false
The standard error of a sampling distribution and the standard deviation of a single data set are the same thing.
false
We want random samples to have at least 10 successes and at least 10 failures if we want our random sample percent to estimate the population percent. This means we will always need a data set size of exactly 20 (for proportions).
false
A 1993 survey conducted by the local paper in Kansas City, Missouri, one week before election day asked voters who they would vote for in the City Attorney's race. Thirty-seven percent said they would for the Democratic candidate. On election day, 41% actually did vote for the Democratic candidate. The 41% is a
parameter
A phone-in poll conducted by a newspaper reported that 64% of those who called in watched the TV show South Park on Comedy Central. The unknown true percentage of American citizens who watch South Park is a
parameter.
A phone-in poll conducted by a newspaper reported that 64% of those who called in watched the TV show South Park on Comedy Central. The number 64% is a(n)
statistic.
The sampling distribution of a statistic is
the distribution of values taken by a statistic in all possible samples of the same size from the same population.
The distribution of the values taken on by a statistic in all possible samples from the same population is called
the sampling distribution
The variability of a statistic is described by
the spread of its sampling distribution.
As the confidence level increases the margin of error will increase.
true
As the sample size increases the margin of error will decrease.
true
Even if a sample is random, the sample value may not be exactly the same as the population value.
true
If we have samples less than 30, we can still use the sample if the sample is nearly normal if we want our random sample means to estimate the population means.
true
It is possible to estimate the standard error from a sampling distribution instead of using the formula
true
Random samples should be at least 30 or nearly normal if we want to use the mean of our random sample to estimate the population means.
true
Random samples will often give very different sample means and sample percentages.
true
The average (center) of all the random sample means will be a good estimate of the population mean.
true
The average (center) of all the random sample percentages (proportions) will be a good estimate of the population percent.
true
The concepts of 95% confidence, confidence intervals, standard error and margin of error can all be better understood from studying sampling distributions.
true
The distribution of random sample means is normally distributed for sufficiently large samples.
true
The margin of error is usually larger than the standard error.
true
The sample percentage from the center of a sampling distribution of sample percentages is much closer to the actual percentage than from a single data set, usually.
true
To create a sampling distribution, take lots of random samples, calculate all their sample means or sample percentages and then make a graph of all the sample means or sample percentages.
true
We want our random samples of size at least 30 if we want our random sample means to estimate the population means.
true
When constructing a confidence interval for a sample proportion, you should use which distribution?
z- distribution