Samples

Definition

Samples are subsets of a population used to represent the entire group. They are essential in statistics for making inferences about the larger population without examining every member.

5 Must Know Facts For Your Next Test

1. A sample should be randomly selected to avoid bias and ensure representativeness.
2. The size of a sample can affect the accuracy and reliability of statistical conclusions.
3. There are different types of sampling methods, including simple random sampling, stratified sampling, and cluster sampling.
4. Sampling error is the difference between the sample statistic and the actual population parameter.
5. Larger samples tend to provide more reliable and valid results compared to smaller samples.

Review Questions

• Why is it important for a sample to be randomly selected?
• How does sample size impact statistical analysis?
• What is sampling error, and why does it occur?

Related terms

Population:

The entire group from which a sample is taken for statistical analysis.

Sampling Methods: Various techniques used to select samples, such as random sampling or stratified sampling.

Sampling Error: The difference between a sample statistic and its corresponding population parameter due to the fact that only a subset of the population is surveyed.