A) The measure of confidence in the null hypothesis
B) The significance level for accepting the null hypothesis
C) The population parameter being tested
D) The probability of obtaining results at least as extreme as the observed results, given that the null hypothesis is true

A) Mann-Whitney U test
B) t-test
C) Wilcoxon signed-rank test
D) Kruskal-Wallis test

A) To test for differences in means
B) To examine the relationship between variables
C) To identify outliers in a dataset
D) To summarize categorical data

A) The strength and direction of a linear relationship between two variables
B) The central tendency of a dataset
C) The variability within groups
D) The spread of the data

A) To compare two independent groups
B) To predict future data points
C) To estimate the range within which the population parameter is likely to fall
D) To determine the probability of an event occurring

A) Convenience sampling
B) Systematic sampling
C) Cluster sampling
D) Simple random sampling

A) Ridge regression.
B) Polynomial regression.
C) Logistic regression.
D) Linear regression.

A) The measure of correlation between two variables
B) The level of confidence in the alternative hypothesis
C) The probability of rejecting the null hypothesis when it is actually true
D) The margin of error in the sample mean

A) Time series analysis.
B) Cluster analysis.
C) Factor analysis.
D) Regression analysis.

A) ANOVA.
B) Regression analysis.
C) T-test.
D) Chi-square test.

A) William Sealy Gosset
B) RAND Corporation
C) John Tukey
D) Carlo Lauro

A) Exclusively in social data science.
B) Strictly within computational linguistics.
C) Econometrics.
D) Only in data science.

A) To state that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases
B) To determine the variability within groups
C) To compare two different samples
D) To calculate the range of a dataset

A) Kernel density estimation
B) Monte Carlo method simulation
C) Artificial neural networks
D) Markov chain Monte Carlo methods

A) Outlier detection.
B) Feature engineering.
C) Imputation.
D) Normalization.

A) The hypothesis that the researcher believes to be true
B) A statement that there is no significant difference between specified populations
C) A statement that predicts an outcome in an experiment
D) The hypothesis that is tested using a one-tailed test

A) The jackknife method.
B) Kernel density estimation
C) Markov chain Monte Carlo methods
D) Artificial neural networks

A) Chi-square test
B) T-test
C) Regression analysis
D) ANOVA

A) Optimization
B) Generating draws from a probability distribution
C) Bayesian updating
D) Numerical integration

A) Bootstrap method
B) Monte Carlo method
C) Markov Chain Monte Carlo
D) Maximum likelihood estimation

A) Culinary arts.
B) Traditional painting techniques.
C) Classical music composition.
D) Computational physics.

A) Exact analytical solutions
B) Numerical integration
C) Generating draws from a probability distribution
D) Optimization

A) A probability density
B) An error function
C) A likelihood function
D) A random sample

A) American Medical Association.
B) World Health Organization.
C) International Linguistics Society.
D) International Association for Statistical Computing.

A) John Tukey’s jackknife
B) RAND Corporation tables
C) ERNIE
D) Monte Carlo simulation device

A) Transforming raw data into knowledge using computer-intensive methods.
B) Avoiding the use of computers in statistical analysis.
C) Focusing solely on small sample sizes.
D) Developing new mathematical theories without practical application.

A) Correlation measures the strength of a relationship, while causation measures the direction
B) Correlation indicates a relationship between variables, while causation implies one variable causes a change in the other
C) Correlation is used for categorical data, while causation is used for continuous data
D) Correlation refers to linear relationships, while causation refers to non-linear relationships