Data types in statistics: qualitative (categorical) and quantitative (numerical). Qualitative: nominal (categories without order like colors) and ordinal (ordered categories like ratings). Quantitative: discrete (countable like frequency) and continuous (measurable like height). Key concepts: identify type for selecting appropriate analysis method. Nominal data: mode only; ordinal: mode and median; quantitative: all measures valid. Common traps: treating ordinal as quantitative, assuming intervals are equal. Exam tips: carefully read variable descriptions, note measurement scale. Time-saving: classify variable before choosing analytical tool. Applications: survey data, market research, quality control. Understanding hierarchy: categorical < ordinal < interval < ratio. Data type determines valid statistical operations. Practice identifying types in real scenarios. Proper classification enables correct analysis. This foundation crucial for statistics mastery.