Sampling

Sampling is the process of selecting a subset of data points from a larger dataset for analysis. It is commonly used when working with large-scale data to reduce computation time and resources while still obtaining meaningful insights. By analyzing a representative sample, you can make accurate inferences about the full dataset without needing to process every data point.

Also known as: Data sampling, statistical sampling.

Comparisons

• Sampling vs. Full Data Analysis: Full data analysis processes every data point, whereas sampling focuses on a subset, making it more efficient.

• Sampling vs. Aggregation: Sampling selects a portion of data, while aggregation summarizes all data for a high-level overview.

Pros

• Reduced computational load: Sampling minimizes time and resource use, especially when handling large datasets.

• Quick insights: Provides faster analysis by processing only a fraction of the full dataset.

• Maintains accuracy with the right sample size: Properly selected samples can still yield highly accurate results.

Cons

• Risk of bias: Poorly selected samples may not represent the entire dataset, leading to inaccurate conclusions.

• May miss important outliers: Rare but critical data points can be excluded from the sample.

• Approximate, not exact: Sampling provides estimations, which may not reflect the full dataset’s exact characteristics.

Example

A marketing team analyzing customer data selects a random sample of 5,000 customers from a pool of 100,000 to evaluate purchasing behavior without processing the entire dataset.