Sample size

Definition

Sample size refers to the number of individual observations or data points collected in a study to represent a larger population. A larger sample size typically leads to more reliable results, as it reduces the margin of error and increases the power of statistical tests, making it easier to identify true effects or differences within the data.

5 Must Know Facts For Your Next Test

1. In general, larger sample sizes provide more accurate estimates of population parameters, reducing variability in results.
2. Sample size calculations often consider factors like the desired confidence level and effect size to determine the minimum number of observations needed.
3. A small sample size can lead to misleading conclusions due to increased risk of sampling error, where results may not accurately reflect the population.
4. In practice, researchers often face trade-offs between obtaining a large enough sample size and practical constraints like time and budget.
5. Different research designs and objectives may require different sample sizes; for example, experimental studies may need larger samples than observational studies to achieve statistical significance.

Review Questions

• How does increasing the sample size affect the reliability and validity of a study's results?
  • Increasing the sample size enhances the reliability and validity of a study by providing more data points that better represent the population. A larger sample reduces variability and the margin of error, leading to more accurate estimates of population parameters. This means that findings from the sample are more likely to reflect true effects or trends within the larger group being studied.
• What factors should researchers consider when determining an appropriate sample size for their study?
  • Researchers should consider several factors when determining an appropriate sample size, including the desired level of confidence, expected effect size, and the potential for attrition or non-response. Additionally, they should assess available resources such as time and funding since these can impose practical limitations on how large a sample can be realistically collected. Balancing these factors helps ensure that the study is both feasible and statistically robust.
• Evaluate how insufficient sample sizes can lead to erroneous conclusions in research and discuss strategies to mitigate this issue.
  • Insufficient sample sizes can lead to erroneous conclusions by increasing the likelihood of Type I or Type II errors, where researchers either incorrectly reject a true null hypothesis or fail to detect a real effect. This may result in misleading findings that do not accurately represent the population. To mitigate this issue, researchers can conduct power analyses prior to data collection to determine an adequate sample size based on expected effects and variability. Additionally, using techniques like bootstrapping or increasing data collection efforts can also help improve reliability when faced with small samples.

Related terms

Population: The entire group of individuals or instances about whom we want to draw conclusions in a study.

Margin of Error: A statistic that expresses the amount of random sampling error in a survey's results, often related to sample size.

Statistical Power: The probability that a statistical test will correctly reject a false null hypothesis, which is influenced by sample size.