Random sample

Definition

A random sample is a subset of individuals selected from a larger population in such a way that each member of the population has an equal chance of being chosen. This method ensures that the sample is representative of the entire population, allowing for more accurate and reliable conclusions to be drawn. In sampling surveys, using a random sample minimizes biases that could affect the validity of the results.

5 Must Know Facts For Your Next Test

1. Random sampling helps eliminate selection bias, making results more reliable and generalizable to the whole population.
2. The larger the random sample size, the more accurate the estimation of population parameters will be, assuming other factors remain constant.
3. Random samples can be obtained using various techniques, such as simple random sampling, systematic sampling, or stratified sampling.
4. In practical terms, generating a random sample can be done using random number generators or drawing lots to ensure fairness.
5. For accurate results, it's crucial that every member of the population has an equal opportunity to be selected in order for the sample to be truly random.

Review Questions

• How does a random sample contribute to the reliability of survey results?
  • A random sample contributes to the reliability of survey results by ensuring that every individual in the population has an equal chance of being selected. This reduces selection bias and allows for a more accurate representation of the population. By minimizing systematic errors, conclusions drawn from the data are more likely to reflect true population characteristics, which is essential for making valid inferences.
• Discuss the implications of using a non-random sample in survey research.
  • Using a non-random sample can lead to significant biases in survey research. When certain individuals or groups are more likely to be included or excluded from the sample, the results may not accurately reflect the views or characteristics of the overall population. This can result in misleading conclusions and poor decision-making based on flawed data. It's crucial for researchers to recognize the potential pitfalls of non-random sampling methods and strive for randomness in their selections.
• Evaluate how different sampling methods can affect the validity of research findings and suggest best practices for achieving a truly random sample.
  • Different sampling methods can significantly impact the validity of research findings by either introducing bias or ensuring accurate representation. For example, convenience sampling can lead to skewed results, while stratified sampling may provide better representation if executed properly. Best practices for achieving a truly random sample include clearly defining the target population, utilizing random selection techniques such as random number generators, and ensuring that all members have an equal opportunity for selection. Implementing these practices will enhance the credibility and reliability of research outcomes.

Related terms

Population:

The entire group of individuals or instances about whom we seek to draw conclusions.

Sampling Error: The error that occurs when a sample does not perfectly represent the population, often due to chance.

Stratified Sampling:

A sampling method that involves dividing the population into subgroups and randomly selecting samples from each subgroup to ensure representation.