Sample-based

Referring to or relying on the analysis or utilization of samples to draw conclusions, make inferences, or represent a larger population or phenomenon. This approach involves selecting and examining a representative portion of a whole, allowing for insights into the characteristics, behaviors, or properties of the entire dataset. sample-based methods are crucial in various fields, including statistics, market research, scientific studies, and data analysis, offering a cost-effective and efficient way to gain knowledge when complete data acquisition is impractical or impossible. The representativeness of the sample is crucial for the validity of the results, which is determined by the sampling methodology.

Sample-based meaning with examples

• In a survey, market researchers use sample-based methods to assess consumer preferences. They distribute questionnaires to a carefully selected subset of the target population. The results, when analyzed and extrapolated, provide valuable insights into product demand and consumer behavior. This sample-based approach, with the right techniques, is vital for cost-effectiveness in marketing research.
• Scientists employ sample-based techniques when investigating the impact of climate change. They analyze ice core samples from glaciers, and tree ring growth to obtain a representative understanding of the climate record over extended periods. These analyses support projections, and allow informed decision-making and assessment of global trends based on past, representative samples.
• To test the effectiveness of a new drug, medical researchers use a sample-based methodology. They administer the drug to a group of patients with similar conditions, and then compare the results with a control group. This rigorous, sample-based, approach helps determine drug efficacy, safety and side-effects, forming the basis for approval.
• In data mining, sample-based algorithms might be employed to reduce the computational burden of analyzing enormous datasets. Rather than processing every single data point, the system extracts a representative subset. Analysis of this smaller sample allows for quicker identification of patterns, trends, and anomalies without significantly compromising the accuracy.