Identify differences within data by sorting, grouping, and organizing characteristics. Use statistical and simulation methods to represent and analyze variability, connecting it to real-world uncertainty and probabilistic processes.

Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.

Recognize measurement errors and natural variability in data. Assess data quality, identify outliers, and refine models using statistical and contextual analysis.

Explore how AI model outputs vary based on training data, labeling, and bias. Understand how generative AI and pre-trained models use large datasets to make inferences and how variability in data impacts outcomes.