Guide

Performance review bias: 5 types and how to design them out

When ratings reflect the reviewer’s personality rather than actual output, promotions and bonuses go to people who are liked, not people who perform. Here are the five biases that do the most damage, and the process changes that remove them.

The 5 biases

Recency bias

What it looks like: Managers judge employees on their last couple of weeks of work instead of output over the whole cycle. Without real-time data, reviews default to grading recent events.

How to fix it: Pre-fill scorecards with real-time and historic data (goals, tickets, commits, feedback) and summarise a whole cycle of 1:1 notes before the review starts.

Central tendency

What it looks like: Defaulting everyone to 3/5 to play safe and avoid tough conversations. You can no longer tell top performers from the rest.

How to fix it: Use observable Yes/No behaviours and a 5-grade scale with clear distinctions, so ratings capture genuine performance variance.

Halo and horn effect

What it looks like: A single high or low area skews the entire review score.

How to fix it: Score goals, skills and values separately, with recommended grades auto-calculated from data across all three.

Leniency

What it looks like: Some managers habitually over-rate their teams, inflating grades.

How to fix it: Calibration flags such as “Manager Leniency” (a historical habit of over-rating) and “Grade not supported by deliverables” (e.g. an “Exceeding” recommendation when task completion was only “Developing”).

Apples-to-oranges standards

What it looks like: Uneven grading bars across managers for identical output: a 4/5 means meeting expectations to one manager and saving a failing project to another.

How to fix it: A shared skills library and universal anchors, so a high performer in one team is held to the same standard as one in another.

Three structural fixes

1. Replace rating scales with observable Yes/No behaviours

Break skills and values into specific, observable milestones assessed on a strict Yes/No basis. An employee either demonstrates the behaviour or doesn’t, which leaves far less room for argument than a 1–5 scale. See employee performance rating scales explained.

2. Calibration flags and mandatory justification

Let data surface the outliers before the meeting: lenient managers, grade inflation, and grades not supported by deliverables. Any manual change to a system-recommended grade should need a documented rationale with evidence. See how to run a performance calibration meeting.

3. The dual-manager system (optional)

So one biased supervisor can’t bottleneck a career, the final grade can average inputs from two leaders: a line manager (focused on delivery) and a functional manager (a discipline expert focused on quality and craft). This helps neutralise individual favouritism.

Check your own process with the free performance review bias checker (you can also paste in a handful of ratings to spot central tendency or leniency), take the 2-minute performance health check, or start from the performance review template.

FAQ

What is recency bias in performance reviews?

Judging an employee on their most recent weeks of work instead of their output over the whole cycle, usually because the manager is relying on memory rather than data.

What is central tendency bias?

Defaulting everyone to the middle of the scale, such as 3 out of 5, to play safe and avoid tough conversations.

What is the halo or horn effect?

When one strong or weak area skews the entire review score.

How do you reduce bias in performance reviews?

Base reviews on full-cycle data, replace subjective rating scales with observable Yes/No behaviours, use calibration that flags lenient managers and unsupported grades, require written justification for manual changes, and consider a second, functional manager’s input.

Capabilities and customer results as described in the Revolut People executive overview (2026). Rewritten in our own words.