Better teacher evaluation should lead to better instruction and improved outcomes for students, but more accurate classification of teachers requires better information than is now available. Because existing measures of performance are incomplete and imperfect, measured performance does not always reflect true performance. Teachers who are truly high-performing may be classified as lower-performing and vice versa. Teacher classification is of course a necessity, and policymakers and practitioners must decide who is eligible to teach in which classrooms and how teachers are promoted or compensated. School leaders have commonly used such measures as academic degrees, teaching experience, classroom observation ratings, and assessments by principals to make these decisions. Less common is the use of "value-added" estimates. Value added measures, however, are being used more and more as data systems are better able to make that student-teacher link. As they are, the measures are raising concerns about the implications for misclassification. Given the risk of misclassification, it is tempting to limit the number of personnel decisions that rely on these imperfect measures. Yet personnel decisions are unavoidable, and they hold significant potential for improving schools. This knowledge brief presents a discussion of what is known about errors in classifying teacher effectiveness, and asks what more needs yet to be known about this issue. The brief touches on what cannot be resolved by empirical evidence on this issue, and advises how and under what circumstances this issue should impact the decisions and actions that districts can make based on teacher evaluations. Better measures of teacher effectiveness, both value-added and other kinds, could reduce both types of error. Given that misclassification is inescapable, reducing the consequences of the problem is an important additional goal and one with potentially large payoffs--particularly for students.