Sample size

Definition

Sample size refers to the number of individual observations or data points collected for a study. It plays a crucial role in research, influencing the reliability and validity of results. A well-chosen sample size can enhance the ability to generalize findings to a larger population, while too small or improperly selected samples can lead to inaccurate conclusions.

5 Must Know Facts For Your Next Test

1. A larger sample size generally leads to more reliable and accurate results, reducing the margin of error.
2. Determining the appropriate sample size involves considering factors such as the population size, desired confidence level, and expected effect size.
3. In probability sampling methods, sample size is crucial for ensuring that each member of the population has an equal chance of being selected.
4. For snowball sampling, sample size can be limited by how many initial respondents are willing to refer others, which may impact the diversity of the sample.
5. In surveys using questionnaires, a well-defined sample size helps ensure that findings are representative and can be generalized to the broader population.

Review Questions

• How does sample size affect the reliability and validity of research results?
  • Sample size directly impacts the reliability and validity of research results because larger samples tend to produce more accurate estimates that are reflective of the larger population. When researchers use a sufficient sample size, it minimizes sampling error and increases confidence in their findings. Conversely, a small or improperly selected sample can lead to biased results and limit generalizability.
• Discuss how sample size considerations differ between probability sampling methods and snowball sampling.
  • In probability sampling methods, determining an appropriate sample size is crucial to ensure that every individual has an equal chance of selection, which helps enhance representativeness. On the other hand, snowball sampling relies on initial participants to refer others, leading to potentially smaller sample sizes. This method may limit diversity and introduce bias because it often captures individuals within specific social networks rather than representing the broader population.
• Evaluate how sample size influences both quantitative and qualitative research methodologies and their respective outcomes.
  • Sample size plays a critical role in both quantitative and qualitative research methodologies but in different ways. In quantitative research, larger sample sizes contribute to statistical power and enable researchers to detect meaningful effects while ensuring results are generalizable. In contrast, qualitative research often focuses on smaller samples to gather deep insights from specific contexts. However, even in qualitative studies, an appropriately defined sample size can enhance credibility by providing sufficient data saturation without overgeneralizing findings.

Related terms

Population: The entire group of individuals or instances about whom researchers want to draw conclusions.

Sampling Error: The difference between the results obtained from a sample and the actual parameters of the population from which it was drawn.

Margin of Error: A statistic expressing the amount of random sampling error in a survey's results, reflecting the degree of uncertainty in the estimate.