Does Practice Really Make Perfect?

You have probably heard or read about Malcolm Gladwell’s “10,000 hour rule”. If you practice doing something for 10,000 hours you will become an expert. You could do this in just under 5 years if you put in 8 hours a day 5 days a week (or 5½ hours every day). Why not get started right now?

This all sounds about right. The most successful people in many areas have indeed put in many hours of practice. But is this why they are so successful?

Let’s consider some basic logic:

  • A necessary condition (for example, putting in lots of practice) must be met in order to achieve a certain outcome (for example, performing at an expert level).
  • A sufficient condition, if met, guarantees a certain outcome.

Practicing in order to become an expert is probably necessary in many cases. But is it actually sufficient?


To answer this question, a group of researchers recently analyzed data from more than 100 studies including over 10,000 participants in a variety of different performance domains.

The results show that practice has relatively small effects. While the amount of practice you put in certainly does explain some variations in performance (about 12% overall) it does not account for most of it.

Practice and Performance

There were also some interesting differences between areas of performance:

  • Practice matters a lot more for game players (such as Chess and Scrabble), musicians, and athletes than it does for others.
  • Practice seems to matter very little for professionals (such as programmers, pilots, and salespeople) or students.

While it may be necessary in some of these cases, we can safely conclude that practice alone is not sufficient.


What now? How can we predict if someone will be good at something?

Looking at a person’s resume usually won’t tell you much about how likely they are to perform. Simply knowing what a person has done in the past can only give you clues about the actual quality of their experience. And there is no reason why a person who hasn’t done something in the past cannot necessarily do that type of thing in the future.

If you want to figure this out, it is usually best to focus on a person’s potential. For example, is there a good match between their personality and the area of performance?

Check out this link for more about factors that predict performance, and this article that I wrote about strategies Google uses to predict performance.


  1. Avatar Anna Pfeffer   •  

    While it’s true that past activity does not guarantee future success and employers should absolutely look at other indicators (besides years of experience), when evaluating their candidacy, there is something missing here.

    It’s the distinction between “practice” and “deliberate practice”. You have used a graphic here that employs the term “deliberate practice”, but I’m afraid that you’ve minced the two very different concepts together. Deliberate practice is not just about showing up to the office for years at a time and then slapping it down on one’s resume, like many knowledge workers do. It’s about a very deliberate drive to improve your skills, your outcomes, your productivity, your impact, your efficiency AND it’s about soliciting constant feedback on your work in order to improve it. It’s not just “10 plus years of sales / hiring / management / coding experience”.

    The thing with athletes for instance – their very deliberate practice is also very measurable. Athletes win races and competitions and score for their teams. Each Word Cup player has a train of statistics following their profile: the number of defensive blocks, successful passes, shots on goal, goals in the WC, goals for their club team, etc. And when all the players are put against each other, the best candidates are easily visible.

    But it’s not so simple with knowledge workers. I wish it were more obvious, but for now, I’d say that if you are an employer – the thing to look for – Is this candidate eager to learn? Does this candidate relish feedback and have a stated goal for improving their skill set? How has this candidate approached relevant problems in the past? Do they have mentors or people they look to for guidance and personal growth? What have they done to keep up with important trends in the given field?

    So my answer the title of this article – no, plain old practice doesn’t necessarily make perfect, but deliberate practice can. The key for the employer during the hiring process is to dig up the approach to that practice, rather than focus on the number of years that person has been in practice.

    “As I’ve discovered, musicians, athletes, and chess players, among others, know all about deliberate practice, but knowledge workers do not. Most knowledge workers avoid the uncomfortable strain of deliberate practice…, a reality emphasized by the typical cubicle dweller’s obsessive e-mail-checking habit – for what is this
    behavior if not an escape from work that’s more mentally demanding?” p. 208 Cal Newport’s book, “So Good They Can’t Ignore You” which addresses the distinction I have made above a heck of a lot more eloquently.

    • Greg Willard Greg Willard   •     Author

      Thanks for the thoughtful comments!

      The research that I describe in this post is an analysis including all known scientific studies of deliberate practice–hence my use of the term in the graphic, as you pointed out. (No non-deliberate practice studies were included in the analysis).

      The reason I didn’t get into the practice vs. deliberate practice distinction in this post is because so many people have heard of the more simplistic “10,000 hour rule” and I wanted to at the very least disabuse that notion before hitting the TL;DR line.

      Interestingly though, the results of this analysis are also quite different from your intuition. Studies using the most objective performance measures show the smallest correlations between deliberate practice and performance. Studies using expert ratings find the second-smallest effects. The largest effects are very general, like being on a winning team (a team that probably practices a lot, granted) or artificial laboratory versions of the performance domain in question.

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