Association of Clinical Research Professionals (ACRP) Certified Professional Practice Exam

Which of the following statements about precision in data is true?

1. It reflects the consistency of measurements

2. It is not important in statistical analysis

3. It indicates the number of data points collected

4. It should be disregarded in favor of accuracy

The correct answer is: It reflects the consistency of measurements

Precision in data refers to the degree to which repeated measurements under unchanged conditions yield the same results. This consistency is crucial in ensuring that experiments or observations can be replicated and that the data are reliable. When measurements are precise, it means there is a small variation among them, indicating a high level of repeatability, which is essential for validating experimental findings and statistical analyses. For example, if multiple measurements of temperature are taken and they all fall within a narrow range, the data is considered precise. In contrast, if measurements vary widely, they can be seen as imprecise, regardless of how close the average of those measurements is to the true value. This highlights the importance of precision in maintaining confidence in the data analysis and conclusions drawn from that data. Therefore, the statement that precision reflects the consistency of measurements accurately captures its significance in data analysis.