A) The measure of confidence in 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 significance level for accepting the null hypothesis
D) The population parameter being tested

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

A) To examine the relationship between variables
B) To test for differences in means
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 spread of the data
C) The variability within groups
D) The central tendency of a dataset

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

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

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

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

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

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

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

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

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

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

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) The hypothesis that the researcher believes to be true
D) A statement that there is no significant difference between specified populations

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

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

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

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

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

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

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

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

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

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

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