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Technical interviews in IT hiring: why passing isn't doing the job

Written by Amine Ben Asker | Jun 8, 2026 7:00:00 AM

A candidate who shines in the interview. Sharp, relaxed, fast and right on every answer. Three months in, they struggle to ship. Anyone who hires in tech knows the scene.

The problem is that a technical interview in IT hiring rarely measures what it claims to. It assesses how comfortably someone talks about code, not their ability to produce it under real constraints. Passing an interview and doing the job are two different skills, and the first does not predict the second.

This article explains why the gap exists, what a standard interview never sees, and how to structure an assessment that measures real competence instead of stage performance.

 

1. The reality: interviews measure ease, not competence

The technical interview is still the central filter in most IT hiring processes. Yet it rewards one specific type of candidate: the one who stays clear-headed while being watched.

A study by North Carolina State University and Microsoft (Behroozi et al., 2020) showed this plainly. When a developer solves a problem on a whiteboard under an interviewer's gaze, their performance drops by more than half compared to the same exercise done in private. So the interview largely measures performance anxiety, not technical level.

What that reveals:

  • a competent candidate can fail simply because they are observed
  • a smooth talker can hide real gaps
  • quieter or atypical profiles get filtered out by mistake

The filter is not neutral. It selects for one ability, handling the pressure of being watched, that has little to do with the job.

 

2. Why a strong interviewer can fail on the job

Real engineering work looks nothing like an interview. Nobody writes a data structure on a whiteboard, from memory, in fifteen minutes, while a stranger judges every line.

On the job, competence lives elsewhere: reading existing code, diagnosing an incident, navigating an imperfect codebase, collaborating with others. Communication alone takes up a large share of a developer's day, but it never compensates for missing operational ability.

The result? An interview that rewards quick wit and theory makes two symmetrical errors. It validates fluent but weak candidates, and it eliminates solid but anxious ones. Either way, the Hire / No Hire decision rests on the wrong signal.

 

3. What a technical interview never sees

A standard technical interview observes a conversation. It does not see the work.

What stays invisible

  • how the candidate approaches a problem they have never seen
  • their ability to debug under a realistic time constraint
  • their reasoning when they don't have the immediate answer
  • their discipline in a production-like environment

The biases that creep in

  • affinity bias: we favor the candidate who resembles us
  • the halo effect: a good communicator looks more competent than they are
  • inconsistency between interviewers, which makes candidates impossible to compare

The problem is that none of these blind spots is fixed by better questions. As long as you evaluate a discussion rather than a deliverable, the signal stays blurry. Learn more about assessing CI/CD skills.

 

4. What you should actually evaluate

The goal is simple: measure what the candidate will actually do on the job, not how well they talk about it.

The key skills to assess:

  • the ability to diagnose and solve a concrete problem
  • reading and understanding existing code
  • reasoning in the face of the unknown, not just the right answer
  • discipline under production-like conditions

What matters is not the volume of knowledge, but the ability to apply it in a real situation. A candidate who hesitates but reasons soundly is often worth more than one who recites without understanding. Platforms like Scalyz place the candidate in an immersive environment to observe exactly that reasoning.

 

5. How to structure a production-like assessment

According to CodinGame (2024), 85% of IT recruiters already use practical tests. But a test only has value if it mirrors the conditions of the role. Here is how to structure one.

Step 1: start from a real scenario

Build the exercise around a situation the candidate will genuinely face: a failing pipeline, a production bug, a feature to extend inside an existing codebase.

Step 2: let them work independently

Reduce direct observation. The goal is to measure competence, not stress tolerance. An environment where the candidate works alone, with their own tools, reveals their true level.

Step 3: observe the reasoning, not just the result

Ask for the choices, the assumptions, the trade-offs. A sound diagnosis left unfinished tells you more than a perfect solution copied from somewhere.

Step 4: standardize with a scoring rubric

Apply the same criteria to every candidate. A shared scoring rubric reduces subjectivity and finally makes profiles comparable.

 

6. A concrete example

Two candidates for a DevOps role. The first is excellent in conversation: he describes the ideal CI/CD pipeline architecture without missing a beat. The second is more hesitant, less at ease in front of the panel.

Without structure: the first is hired on fluency.

With a hands-on scenario: both are given a GitLab CI pipeline that fails after a Docker update, and thirty minutes to fix it. The first describes what should be done but fixes nothing. The second isolates the cause, tests, fixes, and documents his approach.

The result:

The most convincing candidate in conversation would have been a bad hire. The hands-on scenario flipped the decision in minutes, on the only criterion that truly matters: the ability to solve a real problem.

 

7. FAQ: technical interviews and IT skills assessment

Should you drop the technical interview entirely?

No. The interview still has value for assessing communication and motivation. But it should no longer be the sole filter for the technical decision. Pair it systematically with a hands-on scenario.

How long should a practical assessment last?

Thirty to sixty minutes is enough when the exercise is focused. A short, production-like scenario reveals more than a long academic test disconnected from the role.

How do you reduce bias in assessment?

Standardize. The same scoring rubric, scenario, and criteria for every candidate limit subjectivity and the halo effect. The Hire / No Hire decision then rests on comparable evidence.

Doesn't a practical test disadvantage some profiles?

The opposite, when it's well designed. By reducing direct observation and interview stress, a real-world assessment gives competent but less confident candidates a fair shot.

 

Conclusion :

Passing a technical interview in IT hiring proves one thing: the candidate knows how to pass an interview. It says almost nothing about their ability to hold the role. The gap between the two is exactly where mis-hires happen.

Good hiring doesn't rest on the impression left by a conversation. It rests on watching real work. Measure what the candidate will do, not what they can say about it.

See how Scalyz helps recruiting and engineering teams assess real skills under production-like conditions.

 

 

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