Which method primarily reduces random error in measurements?

• A. Calibrating the instrument only
• B. Ignoring outliers
• C. Using a larger sample size has no effect
• D. Repeating measurements and averaging results

Answer: (D)

Random error in measurements comes from unpredictable fluctuations in the measurement process. Each measurement jiggles a little around the true value due to tiny variations in the instrument, environment, or user. When you repeat the measurement many times and average the results, these random deviations tend to cancel each other out. The average moves closer to the true value because the positive and negative slips balance, reducing the overall spread and giving you a more accurate estimate.

Calibrating an instrument helps with systematic error—the consistent bias that can shift all measurements in one direction—so it improves accuracy in a different way rather than reducing random scatter. Ignoring outliers can distort your data and doesn’t specifically target the random noise you’re trying to minimize. And a larger number of measurements does reduce the uncertainty of your mean (the precision), so saying it has no effect isn’t accurate. The best way to dampen random fluctuations is to repeat and average.