When plotting the calibration curve in an experiment, which of the following practices helps minimize experimental error?

• A. Ignoring outlier data points without justification
• B. Using multiple standard solutions and averaging the measurements
• C. Recording only the highest value obtained for each standard
• D. Plotting the curve without labeling the axes

Answer: (B)

Verified step by step guidance

1. Understand that a calibration curve is used to determine the relationship between known concentrations of a substance and their measured response (e.g., absorbance, voltage). Accurate data points are essential for minimizing experimental error.
2. Recognize that using multiple standard solutions with different known concentrations helps create a more reliable and representative calibration curve, as it covers a range of values rather than relying on a single point.
3. Averaging multiple measurements for each standard solution reduces random errors by smoothing out fluctuations that may occur in individual readings, leading to more precise and consistent data.
4. Avoid ignoring outlier data points without proper justification, as this can bias the results and reduce the integrity of the data set. Instead, investigate the cause of outliers and decide whether to exclude them based on sound reasoning.
5. Always label the axes on the calibration curve plot to clearly indicate what is being measured and the units involved, which helps in interpreting the data correctly and avoiding confusion.