Covers the four BINS conditions that define a binomial setting, the binomial probability formula, and how the distribution's shape, mean, and standard deviation depend on n and p. Students apply exact binomial probabilities to defect-rate monitoring, conversion-rate analysis, and acceptance sampling.

Covers the conditions that define a Poisson setting, the Poisson probability formula, and the distinctive property that its mean and variance are both equal to lambda. Students rescale rates across time/space intervals and apply Poisson probabilities to call-center staffing, insurance claims, and server capacity, closing with a fully worked queueing-theory case study.

Introduces the Normal distribution's bell-curve shape and its two governing parameters, the 68-95-99.7 empirical rule, and Z-score standardization for comparing values across scales. Students compute forward Normal probabilities and inverse Normal percentiles, applying both to quality control, pricing, and Value-at-Risk problems.

Covers the sampling distribution of the sample mean and the standard error, then the Central Limit Theorem — why the sample mean is approximately Normal for large n regardless of the population's shape. Students apply CLT-based probability calculations to sample means and sample proportions, closing with a lab that simulates the theorem empirically.