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

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

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

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

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 predict future data points
D) To compare two independent groups

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A) Correlation indicates a relationship between variables, while causation implies one variable causes a change in the other
B) Correlation measures the strength of a relationship, while causation measures the direction
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