Question 32

Sample sizes are important for ensuring statistical significance because they allow accurate conclusions to be confidently applied to larger populations.

Having an appropriate sample size enhances the reliability of the results obtained from a study, enabling researchers to generalize their findings to a broader context with greater confidence.

A) So that accurate conclusions can be confidently applied to larger populations

This option is correct as it directly addresses the main purpose of having an adequate sample size in research. A larger sample size reduces the margin of error and increases the power of the study, making it possible to draw valid conclusions that can be extrapolated to the larger population.

B) So that a hypothesis cannot be misinterpreted

While sample size plays a role in the clarity of results, this option is incorrect because it does not directly relate to the concept of statistical significance. A hypothesis can still be misinterpreted regardless of sample size; thus, this does not address the core issue of ensuring statistical significance.

C) So that no additional analysis is required

This option is incorrect as it suggests that a sufficient sample size eliminates the need for further analysis, which is not true. Regardless of sample size, further analysis may be necessary to interpret data correctly, and ensuring statistical significance often requires additional statistical methods.

D) So that the possibility of researcher bias is eliminated

This choice is incorrect because sample size alone does not eliminate researcher bias. Bias can occur regardless of how many subjects are included in a study, and addressing bias requires careful design and methodology beyond just the sample size.

Conclusion

In summary, option A is the only choice that accurately reflects the critical role of sample size in research, specifically in ensuring that findings are statistically significant and applicable to a larger population. The other options fail to address the importance of sample size in relation to statistical significance, focusing instead on misinterpretation, analysis, or bias, which are not directly tied to this concept.