Sampling and Confidence Intervals

To make inferences about the entire population

To measure every object in the population

To increase the population size

To eliminate sampling error

How to calculate confidence intervals

The exact size of a population

Different methods of sampling

How to eliminate sampling error

The variation in results from different samples of the same population

The error that occurs when no sample is taken

The error that occurs when measuring all objects in a population

The error that occurs when a sample perfectly represents a population

To show the exact value of the population parameter

To increase the sample size

To communicate the accuracy of the estimate

To eliminate the need for further sampling

The population has little variation

There is greater variation within the population

The sample size is very large

The population parameter is known exactly

Sample size has no effect on confidence intervals

Larger samples lead to narrower confidence intervals

Smaller samples lead to narrower confidence intervals

Larger samples lead to wider confidence intervals

Sampling error remains constant

Sampling error increases

Sampling error decreases

Sampling error becomes irrelevant

The color of the sample objects

Sample size

The level of confidence

Variation within the population

It makes the interval wider

It eliminates the need for a confidence interval

It has no effect on the interval

It makes the interval narrower

To provide a single value for the parameter

To indicate the exact value of the parameter

To show the range in which the parameter likely lies

To avoid using statistical methods