Analysing and Interpreting Data
Estimation, significance testing, and the relationship between two variables.
01Sampling Distributions and EstimationThe distribution of the sample mean and its standard error, the Central Limit Theorem, unbiased estimators, constructing and correctly interpreting a confidence interval, and choosing a sample size.100 questions02Hypothesis TestingSetting up null and alternative hypotheses, one- and two-tailed tests, significance levels and critical regions, the z-test for a mean, p-values, and the two kinds of error a test can make.100 questions03The t-TestWhen the t-distribution replaces the normal, degrees of freedom, the one-sample t-test, confidence intervals using t, the paired t-test for before-and-after data, and the assumptions all of them rest on.100 questions04The Chi-Squared TestComparing observed frequencies with expected ones, computing expected values for a contingency table, the test statistic and its degrees of freedom, testing two variables for independence, and the conditions the test requires.100 questions05Correlation and Linear RegressionScatter diagrams, the product-moment correlation coefficient and what it does and does not measure, Spearman's rank correlation, the least-squares regression line, and using it responsibly for prediction.100 questions