Systematic Errors are consistent, repeatable errors that occur due to flaws or biases in the measurement process, leading to consistent deviations from the true value. Unlike random errors, which vary unpredictably, systematic errors are predictable and can be corrected if identified.

• Calibration Issues: Incorrect calibration of measurement instruments can introduce systematic errors. For example, if a scale is not calibrated correctly, it will consistently produce measurements that are offset from the true value.
• Environmental Factors: External conditions, such as temperature, humidity, or magnetic fields, can affect measurements. If environmental influences are not accounted for, they can introduce consistent deviations in the results.
• Instrument Design: Flaws or limitations in the design of measurement instruments can lead to systematic errors. For instance, a poorly designed thermometer may have a fixed offset in its readings.

• Calibration and Recalibration: Regularly calibrating instruments against known standards can help identify and correct systematic errors. Calibration adjusts the instrument to ensure its measurements are as close to the true value as possible.
• Error Analysis and Adjustment: Systematic errors can be identified through detailed error analysis and comparison with known reference values. Once identified, adjustments can be made to correct for the biases in the measurement process.
• Standard Operating Procedures (SOPs): Implementing and following SOPs helps reduce systematic errors by standardizing measurement techniques and ensuring consistency in the process.

• Impact on Accuracy: Systematic errors affect the accuracy of measurements and can lead to incorrect conclusions or decisions based on faulty data. They can skew results consistently in one direction, impacting the reliability of the data.
• Quality Assurance: In quality assurance and manufacturing, systematic errors can lead to defects or inconsistencies in products. Identifying and addressing these errors is crucial for maintaining product quality and meeting specifications.

• Measurement Bias: A common example is a scale that consistently reads 5 grams more than the actual weight due to calibration issues. This bias affects all measurements in the same way.
• Instrumentation Error: If a thermometer always reads 2°C higher than the actual temperature due to a design flaw, this systematic error will consistently affect temperature readings.

• https://en.wikipedia.org/wiki/Systematic_error
• https://www.techopedia.com/definition/7191/systematic-error