A) The measure of confidence in the null hypothesis B) The population parameter being tested C) The significance level for accepting the null hypothesis D) The probability of obtaining results at least as extreme as the observed results, given that the null hypothesis is true
A) t-test B) Wilcoxon signed-rank test C) Mann-Whitney U test D) Kruskal-Wallis test
A) To examine the relationship between variables B) To identify outliers in a dataset C) To test for differences in means D) To summarize categorical data
A) The central tendency of a dataset B) The spread of the data C) The variability within groups 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 compare two independent groups C) To determine the probability of an event occurring D) To predict future data points
A) Systematic sampling B) Cluster sampling C) Convenience sampling D) Simple random sampling
A) Logistic regression. B) Ridge regression. C) Polynomial regression. D) Linear regression.
A) The probability of rejecting the null hypothesis when it is actually true B) The measure of correlation between two variables C) The level of confidence in the alternative hypothesis D) The margin of error in the sample mean
A) Factor analysis. B) Time series analysis. C) Cluster analysis. D) Regression analysis.
A) T-test. B) Regression analysis. C) ANOVA. D) Chi-square test.
A) RAND Corporation B) John Tukey C) Carlo Lauro D) William Sealy Gosset
A) Exclusively in social data science. B) Only in data science. C) Econometrics. D) Strictly within computational linguistics.
A) To calculate the range of a dataset B) To determine the variability within groups C) To state that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases D) To compare two different samples
A) Monte Carlo method simulation B) Markov chain Monte Carlo methods C) Kernel density estimation D) Artificial neural networks
A) Normalization. B) Feature engineering. C) Imputation. D) Outlier detection.
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) Artificial neural networks B) The jackknife method. C) Markov chain Monte Carlo methods D) Kernel density estimation
A) Chi-square test B) T-test C) ANOVA D) Regression analysis
A) Generating draws from a probability distribution B) Optimization C) Numerical integration D) Bayesian updating
A) Bootstrap method B) Maximum likelihood estimation C) Monte Carlo method D) Markov Chain Monte Carlo
A) Classical music composition. B) Traditional painting techniques. C) Culinary arts. D) Computational physics.
A) Generating draws from a probability distribution B) Exact analytical solutions C) Optimization D) Numerical integration
A) A probability density B) An error function C) A random sample D) A likelihood function
A) International Linguistics Society. B) World Health Organization. C) American Medical Association. D) International Association for Statistical Computing.
A) Monte Carlo simulation device B) ERNIE C) John Tukey’s jackknife D) RAND Corporation tables
A) Avoiding the use of computers in statistical analysis. B) Developing new mathematical theories without practical application. C) Transforming raw data into knowledge using computer-intensive methods. D) Focusing solely on small sample sizes.
A) Correlation refers to linear relationships, while causation refers to non-linear relationships 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 indicates a relationship between variables, while causation implies one variable causes a change in the other |