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Discuss why sampling is used in statistics.

   Discuss why sampling is used in statistics.

Fundamentals of Statistics

Fundamentals of Statistics

Michael Sullivan III 5th Edition

Chapter 1, Problem 3

Instant Answer

Solved by Expert Harsh Gadhiya

06/22/2021

Step 1

In statistics, a sample is a subset of a population. The population refers to the entire group that we are interested in studying, while a sample is a smaller group selected from that population. \[ \text{Sample} \subset \text{Population} \] Show more…

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Key Concepts

Representativeness and Bias

Ensuring that a sample is representative of the population is essential to avoid bias in results. Good sampling techniques are designed to mirror the characteristics of the overall population, thereby minimizing systematic errors and increasing the reliability of conclusions drawn from the sample data.

Cost Efficiency and Practicality

Sampling offers significant cost efficiency and practicality. Collecting data from an entire population can be prohibitively expensive and time-consuming. Instead, well-designed sampling methods enable researchers to obtain high-quality data swiftly and at a lower cost, making studies feasible and efficient.

Statistical Inference

Statistical inference is the process of making generalizations about a population based on observations from a sample. By using sampling, statisticians can apply probability theory to estimate population parameters and test hypotheses, providing a robust framework for decision-making even when full population data is unavailable.

Population and Sample

In statistics, the population refers to the complete set of items or individuals under study, while a sample is a smaller, manageable subset drawn from that population. Sampling is crucial because it allows statisticians to gather data and draw conclusions about a large population without having to assess every single element, which is often impractical or impossible.

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