In performance evaluation, which bias refers to giving undue weight to recent events?

In performance evaluation, which bias refers to giving undue weight to recent events?

• A. Strictness
• B. Leniency
• C. Central tendency
• D. Recency

Answer: (D)

Recency bias is the tendency to overweight recent events when forming judgments about performance. In evaluations, this shows up when the most recent incidents or outcomes—good or bad—dominate the overall rating, even if earlier performance was steady or strong. For example, a string of recent achievements or a few recent mistakes can disproportionately influence the final assessment, making the evaluator overlook the longer-term track record. This differs from strictness bias (rating everyone too harshly), leniency bias (rating everyone too leniently), or central tendency bias (avoiding extreme ratings and clustering around the middle). Those biases affect scoring patterns more uniformly across time, whereas recency bias specifically skews judgments toward the latest observations. To counter it, use a consistent, data-driven evaluation framework that considers performance across the entire period, documenting patterns over time and calibrating ratings with predefined criteria.

Recency bias is the tendency to overweight recent events when forming judgments about performance. In evaluations, this shows up when the most recent incidents or outcomes—good or bad—dominate the overall rating, even if earlier performance was steady or strong. For example, a string of recent achievements or a few recent mistakes can disproportionately influence the final assessment, making the evaluator overlook the longer-term track record.

This differs from strictness bias (rating everyone too harshly), leniency bias (rating everyone too leniently), or central tendency bias (avoiding extreme ratings and clustering around the middle). Those biases affect scoring patterns more uniformly across time, whereas recency bias specifically skews judgments toward the latest observations. To counter it, use a consistent, data-driven evaluation framework that considers performance across the entire period, documenting patterns over time and calibrating ratings with predefined criteria.