Random sampling

Definition

Random sampling is a method used in research to select a subset of individuals from a larger population, ensuring that each individual has an equal chance of being chosen. This technique is crucial for obtaining unbiased data and enhancing the reliability of both quantitative and qualitative research methods, allowing researchers to make generalizations about the entire population based on the sampled data.

5 Must Know Facts For Your Next Test

1. Random sampling helps reduce selection bias, making research results more valid and representative of the larger population.
2. In order to effectively implement random sampling, researchers often use techniques such as random number generators or drawing names from a hat.
3. The larger the sample size in random sampling, the more likely it is that the sample will accurately reflect the characteristics of the population.
4. Random sampling can be applied in both qualitative and quantitative research, though it is more commonly associated with quantitative studies where statistical analysis is used.
5. It is important to note that while random sampling improves representativeness, it does not guarantee that every aspect of the population will be represented in the sample.

Review Questions

• How does random sampling enhance the validity of research findings?
  • Random sampling enhances the validity of research findings by minimizing selection bias, ensuring that every individual in the population has an equal opportunity to be included in the sample. This equal chance helps researchers obtain a sample that accurately reflects the larger population's characteristics. As a result, findings derived from this sample can be generalized more confidently to the overall population, which is especially important in quantitative research where statistical conclusions are drawn.
• Discuss how stratified sampling differs from random sampling and why a researcher might choose to use stratified sampling.
  • Stratified sampling differs from random sampling in that it involves dividing the population into distinct subgroups or strata before randomly selecting samples from each stratum. A researcher might choose stratified sampling to ensure that specific subgroups within the population are adequately represented in the sample. This method can be particularly useful when certain characteristics are crucial for understanding the research question, allowing for more precise analysis and insights into those subgroups.
• Evaluate the implications of using random sampling versus other non-random sampling techniques on the results of a study.
  • Using random sampling typically leads to more reliable and generalizable results compared to non-random sampling techniques, such as convenience or purposive sampling. While non-random methods can introduce biases that distort findings, random sampling reduces these risks by ensuring every member of the population has an equal chance of selection. Consequently, studies utilizing random sampling are more likely to provide accurate representations of larger populations, making their conclusions more robust and trustworthy in informing decision-making processes.

Related terms

Sample Size: The number of individuals included in a sample, which can affect the accuracy and reliability of research findings.

Stratified Sampling: A sampling method that involves dividing a population into subgroups and randomly sampling from each subgroup to ensure representation.

Population: The entire group of individuals or instances about whom a researcher wants to draw conclusions.