What is the effect of systematic error on data analysis?

Systematic error refers to consistent, repeatable errors that occur in the same direction each time a measurement is taken. This type of error impacts the accuracy of data because it leads to measurements being consistently biased, either overestimating or underestimating the true values.

When systematic errors are present, the results will consistently skew in one direction—either higher or lower than the true value—rather than randomly fluctuating around it. This consistent bias can significantly affect the conclusions drawn from the data by misleading interpretations of the measured values. As a result, the average of the data sets will not accurately represent the true value of what is being measured.

This understanding helps to clarify why the other choices do not accurately describe the effect of systematic error. For example, saying it only affects the standard deviation overlooks the fundamental impact on accuracy. Additionally, claiming it has no effect on results is inaccurate since systematic errors fundamentally distort measurements. Finally, while systematic errors might contribute to inconsistencies in data, their primary characteristic is the consistent bias rather than randomness, which is typically associated with random errors.