Intrinsic error, also known as systematic error, is a type of error that is inherent in the measurement process. This means that the error is not due to random factors, but rather it is consistently present in the measurement, resulting in a systematic bias in the data.

One example of intrinsic error is observer bias, where the individual taking the measurement may be influenced by their own preconceived notions or beliefs, leading to inaccuracies in the measurement. For instance, in a study on the effectiveness of a new medication, the observer may have a positive view of the medication and therefore may be more likely to record positive results, even if the medication is not actually effective. This observer bias can lead to inaccurate conclusions about the effectiveness of the medication.

Another example of intrinsic error is instrument error, where the measuring instrument itself may be flawed or not properly calibrated, leading to inaccurate measurements. For instance, in a study on blood pressure, the blood pressure monitor may not be properly calibrated, resulting in consistently high or low readings. This instrument error can lead to inaccurate conclusions about the individual’s blood pressure and may lead to improper treatment decisions.

Overall, intrinsic error can have significant implications for the accuracy and reliability of research findings. It is important for researchers to carefully consider and control for potential sources of intrinsic error in order to ensure the validity of their data and conclusions. This can be done through various methods, such as using multiple observers or instruments to take measurements, or using standardized procedures to eliminate observer bias. By carefully controlling for intrinsic error, researchers can improve the accuracy and reliability of their findings and provide more accurate and useful information for decision making.