To describe a population based on a sample.

To make predictions about a population based on a sample.

To test hypotheses about a population based on a sample.

To estimate the parameters of a population based on a sample.

A parameter is a measure of the entire population, while a statistic is a measure of a sample.

A parameter is a fixed value, while a statistic is a random variable.

A parameter is known, while a statistic is estimated.

All of the above.

The central limit theorem states that the distribution of sample means approaches a normal distribution as the sample size increases.

The central limit theorem states that the mean of a sample is equal to the mean of the population.

The central limit theorem states that the variance of a sample is equal to the variance of the population.

The central limit theorem states that the standard deviation of a sample is equal to the standard deviation of the population.

A hypothesis test is a statistical procedure used to determine whether a hypothesis about a population is supported by the evidence.

A hypothesis test is a statistical procedure used to estimate the parameters of a population.

A hypothesis test is a statistical procedure used to describe a population.

A hypothesis test is a statistical procedure used to make predictions about a population.

State the null and alternative hypotheses.

Collect data from a sample.

Calculate the test statistic.

Determine the p-value.

Make a decision about the null hypothesis.

All of the above.

The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed test statistic, assuming the null hypothesis is true.

The p-value is the probability of rejecting the null hypothesis when it is true.

The p-value is the probability of accepting the null hypothesis when it is false.

The p-value is the probability of making a Type I error.

A Type I error is rejecting the null hypothesis when it is true.

A Type I error is accepting the null hypothesis when it is false.

A Type I error is making a false positive decision.

A Type I error is making a false negative decision.

A Type II error is rejecting the null hypothesis when it is false.

A Type II error is accepting the null hypothesis when it is true.

A Type II error is making a false positive decision.

A Type II error is making a false negative decision.

The significance level is the maximum p-value at which the null hypothesis can be rejected.

The significance level is the minimum p-value at which the null hypothesis can be rejected.

The significance level is the probability of rejecting the null hypothesis when it is true.

The significance level is the probability of accepting the null hypothesis when it is false.

A confidence interval is a range of values within which the true population parameter is likely to fall.

A confidence interval is a range of values within which the sample statistic is likely to fall.

A confidence interval is a range of values within which the p-value is likely to fall.

A confidence interval is a range of values within which the significance level is likely to fall.

As the confidence level increases, the width of the confidence interval decreases.

As the confidence level increases, the width of the confidence interval increases.

The confidence level and the width of a confidence interval are not related.

The relationship between the confidence level and the width of a confidence interval depends on the sample size.

A chi-square test is a statistical test used to determine whether there is a significant difference between the observed and expected frequencies of a categorical variable.

A chi-square test is a statistical test used to determine whether there is a significant relationship between two categorical variables.

A chi-square test is a statistical test used to determine whether there is a significant difference between the means of two groups.

A chi-square test is a statistical test used to determine whether there is a significant relationship between a categorical variable and a continuous variable.

An ANOVA test is a statistical test used to determine whether there is a significant difference between the means of two or more groups.

An ANOVA test is a statistical test used to determine whether there is a significant relationship between two or more categorical variables.

An ANOVA test is a statistical test used to determine whether there is a significant difference between the observed and expected frequencies of a categorical variable.

An ANOVA test is a statistical test used to determine whether there is a significant relationship between a categorical variable and a continuous variable.

A regression analysis is a statistical technique used to determine the relationship between a dependent variable and one or more independent variables.

A regression analysis is a statistical technique used to determine the difference between the means of two or more groups.

A regression analysis is a statistical technique used to determine the relationship between two or more categorical variables.

A regression analysis is a statistical technique used to determine the difference between the observed and expected frequencies of a categorical variable.