ONLINE GAMES
You have spent years designing experiments, debugging analyses, and defending conclusions against skeptical scientists.
Then McKinsey sends you a game.
You may think:
Finally. An assessment built for analytical people.
Maybe.
But Solve is not asking for your dissertation brain.
The challenge is not showing how sophisticated your reasoning can become.
It is making good decisions inside a new system, quickly, without turning every problem into a research project.
Your PhD advantage appears... and then creates a trap
Research trains you to investigate.
You gather information. Question assumptions. Explore edge cases. Look for mechanisms.
Those instincts can be useful.
But an assessment environment adds something your lab often does not:
a clock that does not care how interesting the mechanism is.
You can understand every variable beautifully and still spend too long deciding what matters.
That is the reversal.
The candidate who knows less may sometimes move faster because they are willing to prioritize sooner.
McKinsey describes Solve as a gamified assessment of problem-solving ability, and the firm says performance is considered together with the rest of the application and other assessments.
So treat it as another source of evidence about how you solve.
For the deeper mechanics and practice approach, see McKinsey Solve Game: Guide & Free Practice.
The dangerous sentence is “I can calculate that”
Of course you can.
You have probably dealt with statistics harder than anything that resembles consulting assessment math.
That is not the point.
Suppose the screen gives you multiple pieces of information.
A PhD instinct can be:
- understand every variable
- verify the relationships
- calculate precisely
- make the decision
A consulting-style instinct is often closer to:
- understand the objective
- identify which information changes the decision
- calculate what is necessary
- move
The difference is prioritization.
You are not trying to publish the result.
You are trying to reach the right decision efficiently.
The skills transfer... but their form changes
| PhD habit | Useful version | The trap |
|---|---|---|
| Build a complete model | Understand the system | Modeling details that do not change the decision |
| Check everything | Catch material errors | Rechecking low-value steps |
| Explore alternatives | Test hypotheses | Treating every option equally |
| Seek precision | Calculate accurately | Refusing to approximate when appropriate |
| Investigate anomalies | Notice important signals | Chasing every interesting irregularity |
None of those research habits are bad.
They simply need a different stopping rule.
That stopping rule is often: Do I have enough to choose?
So should you “prepare” for Solve?
Be careful with what that word means.
It should not mean memorizing leaked scenarios or trying to reverse-engineer proprietary scoring.
McKinsey explicitly restricts using outside assistance, AI tools, websites, pre-prepared notes, screenshots, or other people during the assessment.
Preparation belongs before the assessment.
You can become more comfortable with time pressure, unfamiliar interfaces, prioritization, quantitative reasoning, and hypothesis-driven decision-making.
You can also remove avoidable friction: use a reliable computer, stable internet, and a distraction-free environment.
For tactical preparation principles, read McKinsey Solve/PSG: Proven, Non-Nonsense Tips & Free Trial.
The weirdest advantage your PhD gives you comes last
You already know what it feels like to encounter something you have never seen before.
A broken experiment.
An unexpected result.
A reviewer question you did not anticipate.
That discomfort is familiar.
Use it.
Do not spend the first minutes proving that you understand the entire world on the screen.
Find the objective.
Find the variables that matter.
Make a decision.
Then keep moving.
Your PhD taught you how to go deep.
Solve may be testing whether you know when not to.