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 significance level for accepting the null hypothesis D) The measure of confidence in the null hypothesis
A) Mann-Whitney U test B) t-test C) Wilcoxon signed-rank test D) Kruskal-Wallis test
A) To summarize categorical data B) To identify outliers in a dataset C) To test for differences in means D) To examine the relationship between variables
A) The central tendency of a dataset B) The strength and direction of a linear relationship between two variables C) The spread of the data 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 predict future data points D) To compare two independent groups
A) Simple random sampling B) Cluster sampling C) Convenience sampling D) Systematic sampling
A) Polynomial regression. B) Logistic regression. C) Linear regression. D) Ridge regression.
A) The level of confidence in the alternative hypothesis B) The probability of rejecting the null hypothesis when it is actually true C) The margin of error in the sample mean D) The measure of correlation between two variables
A) Cluster analysis. B) Regression analysis. C) Factor analysis. D) Time series analysis.
A) T-test. B) Chi-square test. C) ANOVA. D) Regression analysis.
A) William Sealy Gosset B) RAND Corporation C) Carlo Lauro D) John Tukey
A) Econometrics. B) Strictly within computational linguistics. C) Exclusively in social data science. D) Only in data science.
A) To compare two different samples B) To calculate the range of a dataset C) To state that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases D) To determine the variability within groups
A) Markov chain Monte Carlo methods B) Monte Carlo method simulation C) Kernel density estimation D) Artificial neural networks
A) Imputation. B) Outlier detection. C) Normalization. D) Feature engineering.
A) A statement that there is no significant difference between specified populations B) The hypothesis that the researcher believes to be true 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) Artificial neural networks C) Markov chain Monte Carlo methods D) Kernel density estimation
A) Chi-square test B) Regression analysis C) ANOVA D) T-test
A) Bayesian updating B) Optimization C) Generating draws from a probability distribution D) Numerical integration
A) Bootstrap method B) Monte Carlo method C) Maximum likelihood estimation D) Markov Chain Monte Carlo
A) Computational physics. B) Classical music composition. C) Culinary arts. D) Traditional painting techniques.
A) Exact analytical solutions B) Optimization C) Generating draws from a probability distribution D) Numerical integration
A) A random sample B) An error function C) A probability density D) A likelihood function
A) World Health Organization. B) International Association for Statistical Computing. C) International Linguistics Society. D) American Medical Association.
A) RAND Corporation tables B) John Tukey’s jackknife C) Monte Carlo simulation device D) ERNIE
A) Avoiding the use of computers in statistical analysis. B) Developing new mathematical theories without practical application. C) Focusing solely on small sample sizes. D) Transforming raw data into knowledge using computer-intensive methods.
A) Correlation indicates a relationship between variables, while causation implies one variable causes a change in the other B) Correlation refers to linear relationships, while causation refers to non-linear relationships C) Correlation measures the strength of a relationship, while causation measures the direction D) Correlation is used for categorical data, while causation is used for continuous data |