To estimate the true value of a parameter.

To test a hypothesis about a population.

To make predictions about future events.

To describe the distribution of a variable.

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

A parameter is a population value, while a statistic is a sample value.

A parameter is a theoretical value, while a statistic is an observed value.

A parameter is a known value, while a statistic is an unknown value.

The sample mean will converge to the population mean as the sample size increases.

The sample proportion will converge to the population proportion as the sample size increases.

The sample variance will converge to the population variance as the sample size increases.

All of the above.

The distribution of sample means will be approximately normal for large sample sizes, regardless of the shape of the population distribution.

The distribution of sample proportions will be approximately normal for large sample sizes, regardless of the shape of the population distribution.

The distribution of sample variances will be approximately normal for large sample sizes, regardless of the shape of the population distribution.

All of the above.

Frequentist statistics is based on the law of large numbers, while Bayesian statistics is based on the central limit theorem.

Frequentist statistics is based on the idea of probability as a long-run frequency, while Bayesian statistics is based on the idea of probability as a degree of belief.

Frequentist statistics uses fixed sample sizes, while Bayesian statistics uses adaptive sample sizes.

All of the above.

The problem that we can never be certain that our inductive inferences are correct.

The problem that we can never be certain that our inductive inferences are generalizable.

The problem that we can never be certain that our inductive inferences are relevant.

All of the above.

Statistics can be used to establish causality.

Statistics can be used to rule out causality.

Statistics can be used to suggest causality.

Statistics cannot be used to say anything about causality.

Correlation is a measure of the strength of the relationship between two variables, while causation is a measure of the direction of the relationship.

Correlation is a measure of the linear relationship between two variables, while causation is a measure of the nonlinear relationship between two variables.

Correlation is a measure of the association between two variables, while causation is a measure of the mechanism that produces the association.

All of the above.

The fallacy of assuming that because the consequent is true, the antecedent must also be true.

The fallacy of assuming that because the antecedent is true, the consequent must also be true.

The fallacy of assuming that because two events are correlated, they must also be causally related.

All of the above.

The fallacy of assuming that because the antecedent is false, the consequent must also be false.

The fallacy of assuming that because the consequent is false, the antecedent must also be false.

The fallacy of assuming that because two events are not correlated, they must also not be causally related.

All of the above.

A hypothesis is a tentative explanation for a phenomenon, while a theory is a well-supported explanation for a phenomenon.

A hypothesis is a specific prediction about a phenomenon, while a theory is a general explanation for a phenomenon.

A hypothesis is based on evidence, while a theory is based on speculation.

All of the above.

Evidence is used to support or refute a hypothesis.

Evidence is used to estimate the true value of a parameter.

Evidence is used to make predictions about future events.

All of the above.

A Type I error is the error of rejecting a true hypothesis, while a Type II error is the error of failing to reject a false hypothesis.

A Type I error is the error of accepting a false hypothesis, while a Type II error is the error of failing to accept a true hypothesis.

A Type I error is the error of making a false positive decision, while a Type II error is the error of making a false negative decision.

All of the above.

The probability of making a Type I error.

The probability of making a Type II error.

The probability of rejecting a true hypothesis.

The probability of failing to reject a false hypothesis.

The probability of rejecting a false hypothesis.

The probability of making a Type II error.

The probability of accepting a true hypothesis.

The probability of failing to accept a false hypothesis.