Analyze large datasets by measuring their central tendency while considering the context and distribution of the data.

Examine dataset variability by applying measures of spread to identify and quantify outliers.

Identify the distribution of data points, including clusters, gaps, symmetry, skewness, and modes. Use these patterns to understand data spread and their impact on measures like the mean and median.

Organize data into frequency tables based on shared characteristics. Summarize data using counts, fractions, relative frequencies, or proportions to enable comparisons and generalizations. Understand the implications of choices made when creating and interpreting frequency tables.

Identify and describe missing data numerically and categorically. Distinguish between missing values and true zeros. Understand how missing data impacts relationships, patterns, and models in data interpretation.

Recognize metadata as information about data, including its source, type, and structure. Use metadata to organize, summarize, and analyze data effectively, supporting interpretation and decision-making.