Sample Size

Definition

Sample size refers to the number of individuals or units selected from a population to be included in a study or survey. It is a crucial factor in determining the reliability and accuracy of the data collected, as it directly impacts the statistical power and generalizability of the findings.

5 Must Know Facts For Your Next Test

1. A larger sample size generally provides more reliable and representative data, as it reduces the impact of random sampling errors.
2. The appropriate sample size for a study depends on factors such as the expected effect size, the desired level of statistical significance, and the anticipated rate of attrition or non-response.
3. Insufficient sample size can lead to low statistical power, making it difficult to detect significant effects or differences, even if they exist.
4. Excessively large sample sizes can be costly and time-consuming, and may not always provide additional meaningful insights.
5. Sample size calculations are often performed during the study design phase to determine the minimum number of participants required to achieve the desired level of statistical power and precision.

Review Questions

• Explain how sample size relates to the reliability and generalizability of survey findings.
  • The sample size is a critical factor in determining the reliability and generalizability of survey findings. A larger sample size increases the likelihood that the sample is representative of the broader population, reducing the impact of random sampling errors. This, in turn, enhances the reliability of the data and the ability to draw accurate conclusions that can be generalized to the entire population. Conversely, a small sample size may not adequately capture the diversity and variability within the population, leading to findings that are less reliable and less generalizable.
• Describe the relationship between sample size and statistical power in the context of public opinion research.
  • Statistical power is the likelihood of detecting an effect or difference in a study if it truly exists. In the context of public opinion research, sample size is directly related to statistical power. A larger sample size increases the statistical power of the study, making it more likely to identify significant differences or patterns in public opinion, even if the effect sizes are small. Conversely, a small sample size reduces the statistical power, making it more difficult to detect meaningful differences or trends in public opinion. Researchers must carefully consider the appropriate sample size to ensure they have sufficient statistical power to draw reliable and valid conclusions about the population's views and attitudes.
• Evaluate the importance of sample size considerations in the design and interpretation of public opinion surveys.
  • Sample size is a crucial consideration in the design and interpretation of public opinion surveys. Researchers must carefully determine the appropriate sample size to ensure the survey findings are reliable, representative, and generalizable to the broader population. A larger sample size enhances the statistical power of the study, allowing for the detection of smaller but meaningful differences in public opinion. Additionally, a well-designed sample can help mitigate the impact of non-response and ensure the survey participants are truly reflective of the target population. When interpreting survey results, the sample size should be considered alongside other factors, such as the sampling methodology and the margin of error, to assess the validity and generalizability of the findings. Ultimately, the sample size plays a pivotal role in the overall quality and credibility of public opinion research.

Related terms

Population:

The entire group or set of individuals, objects, or measurements that are of interest in a particular study or survey.

Sampling: The process of selecting a subset of individuals or units from a population to represent the entire population.

Statistical Power: The likelihood of detecting an effect or difference in a study if it truly exists, which is influenced by the sample size.