Explain the term 'sample size' and its relevance.

Sample size refers to the number of observations or data points collected during a study or experiment. It is essential because it directly influences the statistical power of the analysis and the reliability of the conclusions drawn from the data. A larger sample size can provide more accurate estimates of the population parameters, reduce the margin of error, and enhance the ability to detect significant effects or relationships.

In the context of decision-making and data analysis, having an adequate sample size ensures that the results are representative of the greater population. This representativeness is crucial for generalizing findings and making informed decisions based on data analysis. If the sample size is too small, the results may be misleading or subject to greater variability, ultimately impacting the quality of the decision-making process.

As such, understanding sample size is vital for researchers and analysts, as it contributes to the robustness of the findings and the credibility of the data-driven insights obtained.