Sampling is how we learn about a big system without measuring every bit of it. In analytics that system might be all customer orders, or all app downloads, or all visitors to your website (hello!). Good sampling reduces cost and time while keeping conclusions reliable. Bad sampling quietly guarantees bad decisions.
Historical sampling uses existing records to form an analysis-ready subset. Random sampling of historical records avoids selection bias when the archival process was itself unbiased. Time-based sampling helps when data volume is large and trends change over time. When sampling historical telemetry, check for changes in instrumentation, missingness, and schema drift that can make older records incomparable to newer ones.
When gathering new data, good practice is to decide on sample size using power calculations and precision targets. Matched pairs designs can improve results for smaller effect sizes. Probabilistic recruitment techniques for new data collection include random digit dialing, address-based sampling, or random selection from a user registry. Non random sampling includes convenience, quota, and purposive samples. These are fast and practical for exploratory work, product experiments, or early-stage hypothesis testing, but be wary of inference limitations and be open about them when you share your results.
Mediums for sampling span surveys, sensors, server logs, experiments, and passive client analytics. Each medium carries its own biases. Surveys suffer from response bias and framing effects. Sensors and server logs are high frequency but may reflect only a slice of behavior. Experiments provide causal leverage at the cost of intervention. Combining mediums through triangulation reduces single-source weaknesses and improves robustness of inference.