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

A) Kruskal-Wallis test
B) t-test
C) Mann-Whitney U test
D) Wilcoxon signed-rank 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 spread of the data
B) The strength and direction of a linear relationship between two variables
C) The central tendency of a dataset
D) The variability within groups

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 refers to linear relationships, while causation refers to non-linear relationships
D) Correlation is used for categorical data, while causation is used for continuous data