Question 56

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

A sufficiently large sample size increases the reliability of statistical analysis, enabling researchers to generalize findings beyond the sampled group.

A) So that the possibility of researcher bias is eliminated

While minimizing researcher bias is crucial in research, sample size alone does not eliminate bias. Bias can still occur regardless of how large the sample is, as it pertains to the methods of data collection and analysis rather than the number of participants.

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

This option correctly highlights the purpose of having an adequate sample size. A larger sample size reduces the margin of error and increases the likelihood that the sample accurately reflects the broader population, thus making the conclusions more reliable and applicable.

C) So that no additional analysis is required

This statement is misleading; a larger sample size does not negate the need for further analysis. Statistical analysis is still essential to interpret the data correctly, regardless of sample size.

D) So that a hypothesis cannot be misinterpreted

While a larger sample size can help clarify findings, it does not guarantee that a hypothesis cannot be misinterpreted. Misinterpretation can arise from various factors, including the study design and analytical methods employed.

Conclusion

In summary, option B is definitively correct as it emphasizes the key role of sample size in enhancing the reliability of research findings for broader application. The other options either misinterpret the role of sample size or address unrelated aspects of research methodology, thereby failing to capture the essential reasoning behind the need for adequate sample sizes in achieving statistical significance.