Types of Data & Methods of Representation

Introduction

• Data & variable — Data are the recorded values of one or more variables measured on a set of subjects; a variable is any characteristic that can differ between subjects (height, BP, sex, tumour grade). The very first step before any calculation or plotting is to decide what type of data one is dealing with.
• Why type drives everything — The data type dictates the correct summary statistic (mean ± SD vs median + IQR vs proportion), the correct graph (histogram vs bar vs box-plot), and the valid statistical test (parametric vs non-parametric vs χ²). Misclassifying the data → wrong summary, wrong chart, wrong test.
• Biostatistics context — Biostatistics is statistics applied to health/medical data, driven by a hypothesis → data-gathering → analysis → inference cycle; correct data classification is foundational to both designing the study and choosing its analysis.
• Population vs sample — A population is the entire set of subjects of interest; a sample is a representative subset; the gap between a sample value and the true population value is the sampling error.
• Source of data — Primary data = collected first-hand from the research experiment; secondary data = obtained from indirect sources (published literature, registries, another investigator's dataset).