Covers the foundational vocabulary of inferential statistics — populations, samples, parameters, and statistics — and why a sample's representativeness, not its size, determines its usefulness. Students then learn the four probability sampling methods (simple random, systematic, stratified, cluster) and how to distinguish sampling error from sampling bias, closing with a lab that designs and critiques real sampling schemes.

Covers the three measures of center — mean, median, and mode — the distinct question each answers, and how to calculate each from raw, frequency-table, and grouped data. Students learn why the mean is sensitive to outliers while the median resists them, and apply a two-question decision framework (variable type, distribution shape) to select the correct measure for any dataset.

Covers why a measure of center alone is insufficient and introduces spread as the second essential summary of a distribution. Students calculate range, quartiles, IQR, and the five-number summary; variance and standard deviation (including Bessel's correction); and the coefficient of variation for comparing spread across differently scaled variables — closing with a lab that ranks business portfolios by consistency.

Covers building and interpreting frequency tables and histograms, identifying distribution shape (symmetric, right-skewed, left-skewed, uni/bi/multimodal), and applying the Empirical Rule to approximately normal data. Students construct and compare box plots across groups, then complete a full exploratory data analysis pipeline in the module's applied lab.