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REVIEW
published: 18 August 2020
doi: 10.3389/fpsyg.2020.01134
Edited by:
Benjamin Cowley,
University of Helsinki, Finland
Reviewed by:
Jonathan Wai,
University of Arkansas, United States
Matt Sibbald,
McMaster University, Canada
Roger Lister Kneebone,
Imperial College London,
United Kingdom
*Correspondence:
David Z. Hambrick
hambric3@msu.edu;
hambric3@gmail.com
Specialty section:
This article was submitted to
Performance Science,
a section of the journal
Frontiers in Psychology
Received: 07 December 2019
Accepted: 04 May 2020
Published: 18 August 2020
Citation:
Hambrick DZ, Macnamara BN
and Oswald FL (2020) Is
the Deliberate Practice View
Defensible? A Review of Evidence
and Discussion of Issues.
Front. Psychol. 11:1134.
doi: 10.3389/fpsyg.2020.01134
Is the Deliberate Practice View
Defensible? A Review of Evidence
and Discussion of Issues
David Z. Hambrick1*, Brooke N. Macnamara2and Frederick L. Oswald3
1Department of Psychology, Michigan State University, East Lansing, MI, United States, 2Department of Psychological
Sciences, Case Western Reserve University, Cleveland, OH, United States, 3Department of Psychological Sciences, Rice
University, Houston, TX, United States
The question of what explains individual differences in expertise within complex domains
such as music, games, sports, science, and medicine is currently a major topic of
interest in a diverse range of fields, including psychology, education, and sports science,
to name just a few. Ericsson and colleagues’ deliberate practice view is a highly influential
perspective in the literature on expertise and expert performance—but is it viable
as a testable scientific theory? Here, reviewing more than 25 years of Ericsson and
colleagues’ writings, we document critical inconsistencies in the definition of deliberate
practice, along with apparent shifts in the standard for evidence concerning deliberate
practice. We also consider the impact of these issues on progress in the field of
expertise, focusing on the empirical testability and falsifiability of the deliberate practice
view. We then discuss a multifactorial perspective on expertise, and how open science
practices can accelerate progress in research guided by this perspective.
Keywords: deliberate practice, expertise, talent, skill, individual differences
IS THE DELIBERATE PRACTICE VIEW DEFENSIBLE? A REVIEW
OF EVIDENCE AND DISCUSSION OF ISSUES
Not infrequently, a single theoretical perspective becomes extremely influential in an area of
scientific inquiry, shaping the trajectory of research in the field for years or even decades. More
than 25 years ago, K. Anders Ericsson and colleagues proposed what has arguably become the most
influential theoretical perspective in the scientific literature on expertise and expert performance.
In a pivotal Psychological Review article, Ericsson et al. (1993) theorized that expert performance
reflects a long period of deliberate practice, which they stated “includes activities that have been
specially designed to improve the current level of performance” (p. 368). In studies of violinists
(Study 1) and pianists (Study 2), Ericsson et al. (1993) operationally defined deliberate practice
as “practice alone” with the goal of improving performance. The most accomplished musicians
reported having accumulated an average of around 10,000 h of practice alone by early adulthood,
which was thousands of hours more than the averages for this measure of practice for less
accomplished groups.
Applying their framework to several domains of expertise, Ericsson et al. (1993) concluded
that “individual differences in ultimate performance can largely be accounted for by differential
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amounts of past and current levels of practice” (p. 392). They
further explained:
[H]igh levels of deliberate practice are necessary to attain expert
level performance. Our theoretical framework can also provide a
sufficient account of the major facts about the nature and scarcity
of exceptional performance. Our account does not depend on
scarcity of innate ability (talent) (Ericsson et al., 1993, p. 392).
Reiterating this perspective, Ericsson et al. (2007) stated
that “[i]t is possible to account for the development of elite
performance among healthy children without recourse to unique
talent (genetic endowment)—excepting the innate determinants
of body size” (p. 4). And writing in the Harvard Business
Review, Ericsson et al. (2007) explained, “Our research shows that
even the most gifted performers need a minimum of 10 years
(or 10,000 h) of intense training before they win international
competitions” (p. 119).
Ericsson and colleagues’ perspective, which we refer to as
the deliberate practice view, has had a monumental impact on
expertise research. As of this publication, the Ericsson et al.
(1993) article has been cited nearly 11,000 times in a wide
range of literatures, and there have been nearly 200 theses and
dissertations on deliberate practice in universities around the
world. As portrayed in popular press books such as Malcolm
Gladwell’s (2008) bestseller Outliers: The Story of Success and
Daniel Coyle’s (2009) The Talent Code, Ericsson and colleagues’
research has also had a profound influence on the public’s
thinking about the origins of expertise. Taking his inspiration
from Ericsson et al.’s (1993) findings, Gladwell wrote that
“10,000 h is the magic number of true expertise” (p. 11). In his
own popular press book, Peak: Secrets from the New Science of
Expertise, Ericsson wrote, “Deliberate practice can open the door
to a world of possibilities that you may have been convinced were
out of reach. Open that door” (Ericsson and Pool, 2016, p. 179).
We credit and commend Ericsson and colleagues for their
highly influential work. However, here we will discuss what
we believe are serious concerns with whether the deliberate
practice view is viable as a scientific theory—that is, whether
it is empirically testable and falsifiable. [For a similar type of
review, see Gottfredson’s (2003) critique of Sternberg’s practical
intelligence theory; Sternberg et al. (1995)]. Before doing so,
however, we note two uncontroversial claims about expertise, by
which we simply mean a person’s measurable (i.e., quantifiable)
level of performance in a domain. First, as Ericsson and
colleagues have emphasized (e.g., Ericsson, 2006), expertise is
acquired gradually. In other words, people are not literally
born as experts, innately endowed with the type of specialized
knowledge that underpins high-level skill in activities like hitting
a golf ball, solving math equations, playing an instrument, or
choosing a move in a chess game. Domain-specific knowledge
and skill can only be acquired gradually over time through some
form of training.
The second uncontroversial claim is that training can lead to
large, even massive, improvements in people’s level of expertise
(i.e., domain-relevant performance). This point was amply
illustrated by some of Ericsson and colleagues’ earliest research.
For example, in a classic study, Ericsson et al. (1980) showed that
after more than 200 h of training, a college student improved
his performance in a random digit memorization task from a
typical 7 digits to 79 digits (the world record is currently an
astounding 547 digits)1. In short, notwithstanding the issues
raised in this article, Ericsson and colleagues’ deliberate practice
view has important value in society, serving as a useful reminder
to the layperson that training of some form is necessary to achieve
a high level of performance in a domain.
The controversial question in research on expertise is
not whether some form of training is necessary to explain
intraindividual (i.e., within-person) increases in expertise (it
must be), or whether these increases can be massive (they
can be). Rather, the controversial question is the extent
to which interindividual (i.e., between-person) differences
in accumulated amount of training explain interindividual
differences in expertise (for a discussion of the distinctions
between interindividual and intraindividual variability, see
Molenaar et al., 2003). In statistical terms, what is the direction
(and the magnitude) of the correlation between expertise and
accumulated amount of training? Somewhat counterintuitively,
as Figure 1 illustrates, the necessity of training to explain
intraindividual increases in expertise has no direct implication
for the answer to this question. That is, taking as a given
that the relationship between training and expertise is positive
within individuals, the correlation between training and expertise
between individuals could be positive (top panel), indicating
higher levels of performance for individuals who have engaged in
more training; negative (middle panel), indicating lower levels of
performance for individuals who have engaged in more training;
or zero (bottom panel).
What, then, is the correlation between deliberate practice
and expertise across individuals? The first step in attempting to
answer this question is to operationalize deliberate practice—
that is, to develop measures of deliberate practice based on the
definition of the construct. Unfortunately, as we document in
this article, there remains a great deal of confusion about the
definition of deliberate practice, despite more than 25 years of
research on the topic. As Ericsson and colleagues themselves
recently noted: “It has been common for scientists to be confused
about the definition of DP (deliberate practice)” (Dearani et al.,
2017, p. 1333). Here, we discuss possible sources of this continued
confusion, the confusion around the measurement of deliberate
practice that has resulted (e.g., inclusion criteria for meta-
analyses), and the impact of this confusion on the science of
expertise. We conclude this article with thoughts on how to
advance the scientific study of expertise and expert performance.
For a companion presentation to this article, visit https://osf.io/
buqsk/.
WHAT IS DELIBERATE PRACTICE?
It is undoubtedly the case that different types of domain-relevant
activities vary in their importance for developing expertise. For
example, training under a qualified golf instructor is almost
1http://www.world-memory- statistics.co.uk/disciplines.php
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Hambrick et al. The Deliberate Practice View
FIGURE 1 | Schematic illustration of a positive (top panel), negative (middle
panel), and zero (bottom panel) between-person correlation (as illustrated by
the dashed ellipses) between training hours and expertise, assuming a
positive relationship between the variables within individuals (as represented
by the learning curves for four hypothetical individuals).
certainly more beneficial for improving golf skill than mindlessly
hitting practice balls at a driving range. In their article on
deliberate practice, Ericsson et al. (1993) distinguished among
three forms of domain-specific experience. They described
work as engagement in activities for external rewards (e.g.,
music performances, sports competitions), play as participating
in activities for pleasure (e.g., playing a sport with friends
for recreation), and deliberate practice as a “highly structured
activity, the explicit goal of which is to improve performance”
(Ericsson et al., 1993, p. 368).
However, Ericsson and colleagues have been inconsistent on
critical elements of the definition of deliberate practice, and
consequently it has been unclear what activities do and do
not qualify as deliberate practice. For example, Ericsson et al.
(1993) stated that “the teacher designs practice activities that
the individual can engage in between meetings with the teacher”
(p. 368). A few years later, however, Ericsson (1998) stated that
“Ericsson et al. (1993) proposed the term deliberate practice to
refer to those training activities that were designed solely for the
purpose of improving individuals’ performance by a teacher or
the performers themselves” (p. 84, emphasis added). This latter
statement indicated that deliberate practice, as Ericsson et al.
(1993) originally defined the term, encompasses a broader range
of activities than just teacher-designed practice. Yet, as shown
in Figure 2, in subsequent articles, Ericsson and colleagues
were inconsistent on this critical point, sometimes indicating
that deliberate practice must be designed by a teacher (e.g.,
Ericsson, 2015), but other times stating that it can be designed by
teachers or the “performers themselves” (e.g., Keith and Ericsson,
2007). If deliberate practice must be designed by a teacher, then
presumably it cannot also be designed by performers themselves.
As another example of definitional confusion, it is unclear
from Ericsson and colleagues’ writings whether deliberate
practice must be a solitary activity, or whether it can also be a
group/team activity. Citing research on team sports (Helsen et al.,
1998), Ericsson (2006) observed that “the amount of time spent
in team-related deliberate practice activities correlates reliably
with skill level in team sports” (p. 695). It seems clear from this
observation that there can be team deliberate practice. Recently,
however, Ericsson and Harwell (2019) indicated that this is not
the case, commenting that “it is important to point out that
organized team training may be quite effective in improving
performance, but it does not meet all the criteria for deliberate
practice” (p. 6). This is another apparent shift in the definition of
and criteria for deliberate practice, creating still more confusion
about what the “correct” definition is. In short, it is unclear what
activities do and do not qualify as deliberate practice.
This confusion surrounding the definition of deliberate
practice is not a minor matter—it directly impacts how deliberate
practice is measured in empirical studies and what evidence
(i.e., effect sizes) should be included in a meta-analysis. These
decisions, in turn, directly impact the evaluation of the deliberate
practice view: whether evidence is concluded to support the
view or not. Certainly, definitions of theoretical constructs
can and do evolve over time as science progresses, but the
shifts in the definition of deliberate practice reflected in
Figure 2 do not appear to reflect this sort of progression.
There are meaningful changes even over short spans of
time (e.g., compare Keith and Ericsson’s,2007, description of
deliberate practice with Ericsson’s,2006). Confusion around
the definition of deliberate practice persists in the expertise
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1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
“Ericsson et al. (1993) introduced
the term deliberate pracce to
describe focused and efforul
pracce acvies that are pursued
with the explicit goal of
performance improvement….These
acvies can be designed by
external agents, such as teachers or
trainers, or by the performers
themselves.”
- Keith &Ericsson (2007), p. 136
“Expert performance can,
however, be traced to acve
engagement in deliberate pracce
(DP), where training (oen
designed and arranged by their
teachers and coaches) is focused
on improving parcular tasks.”
- Ericsson (2008), p. 988
“Ericsson et al. (1993) proposed the
term deliberate pracce to refer to
those training acvies that were
designed solely for the purpose of
improving individuals’ performance
by ateacher or the performers
themselves.”
- Ericsson (1998), p. 84
“In disncon from leisurely or normal
job-related experience, Ericsson et al.
defined deliberate pracce as avery
specific acvity designed for an individual
by askilled teacher explicitly to improve
performance.”
- Krampe &Ericsson (1996), p. 333
“The core assumpon of deliberate
pracce…is that expert performance is
acquired gradually and that effecve
improvement of performance requires
the opportunity to find suitable training
tasks that the performer can master
sequenally –typically the design of
training tasks and monitoring of the
aained performance is done by a
teacher or acoach.”
- Ericsson (2006), p. 694
Year
“When individuals engage in a
pracce acvity (typically
designed by their teachers),
with full concentraon on
improving some aspect of their
performance, we call that
acvity deliberate pracce.”
- Ericsson (2007a), p. 1 4
“[D]eliberate pracce requires a
teacher who can provide pracce
acvies designed to help a student
improve his or her performance.”
- Ericsson & Pool (2016), p. 98
“Given the cost of individualized
instrucon, the teacher designs
pracce acvies that the
individual can engage in
between meengs with the
teacher. We call these pracce
acvies deliberate pracce and
disnguish them from other
acvies.”
- Ericsson et al. (1993), p. 368
“When this type of training is supervised and guided by a
teacher, it is called ‘deliberate pracce’—a concept my
colleagues and I introduced in 1993.”
- Ericsson (2015), p. 1472
“In numerous other domains it has been possible to idenfy special
pracce acvies (deliberate pracce) that performers’ teachers or
the performers themselves design to provide opportunies to
improve parcular aspects of their performance in an environment
that allows gradual refinement aer problem solving and repeated
variaons with immediate feedback.”
- Ericsson (2007), p. 1128
“deliberate pracce (Ericsson,
Krampe & Tesch-Römer, 1993)--
acvies designed, typically by
a teacher, for the sole purpose
of effecvely improving specific
aspects of an individual's
performance.”
- “Deliberate Pracce and
Expert Performance; An
updated excerpt from
Ericsson (2000)”, K. Anders
Ericsson’s FSU website, 6/6/20
2019
Deliberate pracce is designed by: a teacher /a teacher typically or oen /a teacher or performers themselves
“Deliberate pracce requires a
teacher who is capable of
individualizing instrucon and
pracce and knowledgeable of
pracce methods with verified
performance outcomes.”
–Ericsson (2020), p. 163
2020
FIGURE 2 | Descriptions of deliberate practice from Ericsson’s writings (1993 – 2020). Adapted from Macnamara and Hambrick (2020) with permission of Springer
Nature; this figure can also be found at https://osf.io/buqsk/.
literature, even as researchers are attempting to investigate the
deliberate practice view.
Challenges to the Deliberate Practice
View
Notwithstanding this confusion over the definition of deliberate
practice, there have been numerous attempts to test the deliberate
practice view. One of the first noteworthy tests came from a
study of chess expertise by Gobet and Campitelli (2007), who
administered a questionnaire to 90 members of a Buenos Aries
chess club to assess lifetime engagement in deliberate practice and
tournament chess rating. The self-reported amount of deliberate
practice (hours of studying alone plus hours of group practice)
correlated positively and moderately with chess rating (r= 0.42).
This is a sizeable correlation by psychological standards—a
“medium” effect size in Cohen’s (1992) widely used classification
scheme (i.e., r= 0.10, small; r= 0.30, medium; r= 0.50, large).
However, this finding also challenges the deliberate practice view
because it means that deliberate practice left a large amount
of the total variance in chess ratings unexplained. To be exact,
a correlation of r= 0.42 between two variables indicates that
one variable explains 18% of the variance in the other variable
(i.e., r= 0.42 ×100 = 18%). In the present case, it must be
assumed that some of this unexplained variance reflects random
measurement error, because neither a measure of deliberate
practice nor a measure of performance can be assumed to be
perfectly reliable (we discuss this issue further below). However,
the correlation was not large enough to suggest that, even after
taking this psychometric artifact into account, participants at
similar levels of chess skill would have reported similar amounts
of deliberate practice. Instead, it suggests that the chess players
varied substantially in the amount of deliberate practice they
required to reach a given level of skill. Indeed, according to
the data, they did: As Gobet and Campitelli (2007) described
in their article, the self-reported estimate of number of hours
of deliberate practice required to reach “master” status in their
sample ranged from 3,016 to 23,608 h—a difference of nearly a
factor of 8. The implication is that although deliberate practice
clearly contributes to individual differences in chess expertise,
other factors must contribute as well, as we discuss further in the
final section of this article.
In our own first effort to test the deliberate practice view
(Hambrick et al., 2014b), we reanalyzed results from expertise
studies in the domains of chess and music. Our specific goal
was to test Ericsson et al.’s (1993) aforementioned claim that
“individual differences in ultimate performance can largely be
accounted for by differential amounts of past and current levels
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of practice” (p. 392, emphasis added). We identified six studies
of chess and eight studies of music that reported a correlation
between a measure of deliberate practice and performance. The
average correlation was r= 0.49 for chess and r= 0.43 for
music before applying the standard psychometric correction for
measurement error variance (unreliability) of the constituent
measures. For deliberate practice, we assumed a reliability
coefficient of 0.80 based on information we could find about the
reliability of this variable, as well as on Tuffiash et al.’s (2007)
statement that “self-report practice estimates repeatedly from
experts in sports and music have reported test-retest reliabilities
at or above 0.80” (p. 129) and Ericsson’s (2013) statement that
“[t]he collected reliability of cumulated life-time practice at
different test occasions in large samples has typically been found
to range between 0.7 and 0.8” (p. 534). For music performance,
we used reliability estimates from the studies, or if not reported,
from studies that collected similar performance measures. The
average amount of reliable variance in expertise explained by
deliberate practice was 34% for chess and 29.9% for music. This is
a substantial amount of variance, but it is not enough to support
the claim that deliberate practice largely accounts for individual
differences in expertise. This claim implies that deliberate practice
should at least explain most of the variance in expertise, and
evidence suggests it does not.
Subsequently, we set out to test the importance of deliberate
practice as a predictor of individual differences in expertise, by
way of a formal and comprehensive meta-analysis (Macnamara
et al., 2014; see also Macnamara et al.’s, 2018, corrigendum for
the article), ultimately reviewing over 11,000 articles. Ericsson
et al. (1993) explained that deliberate practice “includes activities
that have been specially designed to improve the current level
of performance” (p. 368). Accordingly, we defined deliberate
practice as structured activities designed to improve performance
in a domain; and given Ericsson and colleagues’ inconsistency
on whether a teacher is required to design deliberate practice,
we decided to include both teacher- and performer-designed
activities. Identifying 88 studies, we found that deliberate
practice explained 14% of the variance in performance overall,
and 24% for games, 23% for music, 20% for sports, 5% for
education, and 1% for professions. We also determined that
deliberate practice left more of the variance in performance
unexplained than it explained, across a range of possible values
for measurement reliability. We concluded that the “amount
of deliberate practice—although unquestionably important as a
predictor of individual differences in performance from both
a statistical and a practical perspective—is not as important as
Ericsson and his colleagues have argued” (Macnamara et al.,
2014, p. 1615).
In a later meta-analysis that focused on sports (Macnamara
et al., 2016b), we found that the contribution of deliberate
practice to sports performance varied by skill level: Among
elite athletes (e.g., national-level and above), deliberate practice
explained only 1% of the performance variance. Although it must
be assumed that range restriction (another psychometric issue)
would limit the deliberate practice-performance correlation
when considering only elite performers, it is critical to note that
it was Ericsson et al. (1993) themselves who stated that deliberate
practice is still an important predictor of performance differences
at the elite level. In their own words, “Individual differences, even
among elite performers, are closely related to assessed amounts
of deliberate practice” (Ericsson et al., 1993, p. 363, emphasis
added). This finding from the Macnamara et al. (2016b) meta-
analysis on sports is inconsistent with this claim. Furthermore,
we found the relationship between deliberate practice and
performance to be very similar whether the practice activities
were solitary or in a group (see Macnamara et al., 2016b).
In a commentary on our meta-analysis (Macnamara et al.,
2014), Ericsson (2014a) rejected 87 of the 88 studies that we
included in our meta-analysis, claiming that we included studies
that “violated [their] criteria for deliberate practice” (p. 2).
However, in doing so, Ericsson (2014a) rejected numerous studies
that he himself had previously used to explicitly argue for the
importance of deliberate practice (see quotations from Ericsson’s
writings in Table 1). Thus, by any reasonable account, the
standard for evidence concerning deliberate practice had shifted
dramatically. Most perplexingly, in applying this new standard
for evidence, Ericsson rejected several of his own studies of
deliberate practice (e.g., Duffy et al., 2004;Tuffiash et al., 2007;
Duckworth et al., 2011), seeming to undermine the case he had
for decades been attempting to make for the importance of
deliberate practice. Ericsson did not acknowledge that he had
once used these studies he was now rejecting to argue for the
importance of deliberate practice. This evaluation of evidence
challenging the deliberate practice view seems indefensible.
Ericsson’s (2014a) rejection of his own study of darts (Duffy
et al., 2004) and his rejection of Charness and colleagues’ studies
of chess (Charness et al., 2005) were especially noteworthy (see
the Appendix for Ericsson’s varying characterizations of the
Charness studies). Ericsson’s (2014a) stated reason for rejecting
these studies was that they provided “no record of a teacher/coach
supervising all or most of practice” (see Ericsson, 2014b,Table 2).
However, in a chapter published in the very same year, Ericsson
(2014c) used both these studies to argue for the importance of
deliberate practice, stating:
[I]n a study of district, national, and professional
dart players Duffy, Baluch, and Ericsson (2004) found
that solitary deliberate practice was closely related to
performance, whereas the amount of social dart activities
did not predict performance (Ericsson, 2014c, p. 191).
In chess, Charness and his colleagues (Charness, Krampe,
and Mayr, 1996; Charness, Tuffiash, Krampe, Reingold, and
Vasyukova, 2005) have found that the amount of solitary
chess study was the best predictor of performance at chess
tournaments, and when this type of deliberate practice was
statistically controlled, there was no reliable benefit from
playing chess games (Ericsson, 2014c, p. 191)2.
Another inconsistency was Ericsson’s (2014a) rejection of his
own study of spelling bee contestants (Duckworth et al., 2011) for
2There were two studies in this project. Charness et al. (1996) is a chapter and
provides an initial report of data from Study 1 (N= 136 of an eventual N= 239);
Charness et al. (2005) is a journal article and reports the full results (Study 1
N= 239; Study 2 N= 169).
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TABLE 1 | Examples of studies that Ericsson rejected for violating his criteria for deliberate practice but previously used to argue for the importance of deliberate practice.
Study rejected by Ericsson (2014a)
for violating his criteria for
deliberate practice
Previous use of the same study by Ericsson and colleagues to argue for the importance of deliberate practice
Hodges and Starkes (1996)1“Several studies and reviews have since found a consistent relation between performance and amount and quality of
deliberate practice. . .in sports (. . .Hodges and Starkes, 1996. . .).”
-Ericsson (1998, p. 87)
Helsen et al. (1998)1“Research conducted in several domains such as. . .sports (Helsen, Starkes, and Hodges, 1998. . .) suggests that the
amount of accumulated deliberate practice is closely related to an individual’s attained level of performance.”
-Keith and Ericsson’s (2007, p. 136)
Duffy et al. (2004)2“The engagement of the dart-related activities differed between groups for three types, namely playing in league darts,
solitary practice and total deliberate practice. The latter two findings were in line with prior expectations namely; the more
an individual engages in deliberate practice (particularly solitary practice) the more proficient their performance is likely to
be. This finding supports one of the main tenets of Ericsson et al.’s (1993) theory whereby expertise is acquired through a
vast number of hours spent engaging in activities purely designed to improve performance, i.e., deliberate practice.”
-Duffy et al. (2004, pp. 242–243).
“[I]n a study of district, national, and professional dart players Duffy, Baluch, and Ericsson (2004) found that solitary
deliberate practice was closely related to performance, whereas the amount of social dart activities did not predict
performance.”
-Ericsson (2014c, p. 191)
Charness et al. (2005)2“The paper by Charness, Tuffiash, Krampe, Reingold, and Vasyukova [2005]. .. extends an earlier classic chapter by
Charness, Krampe, and Mayr (1996) and examines retrospective estimates by a large sample of chess players about their
training during the development of their skill and expertise. This paper reports the most compelling and detailed evidence
for how designed training (deliberate practice) is the crucial factor in developing expert chess performance.”
-Ericsson (2005, p. 237).
“In chess, Charness and his colleagues (Charness, Krampe, and Mayr, 1996; Charness, Tuffiash, Krampe, Reingold, and
Vasyukova, 2005) have found that the amount of solitary chess study was the best predictor of performance at chess
tournaments, and when this type of deliberate practice was statistically controlled, there was no reliable benefit from playing
chess games.”
-Ericsson (2014c, p. 191)
Tuffiash et al. (2007)2“Several researchers have reported a consistent association between the amount and quality of solitary activities meeting the
criteria of deliberate practice and performance in different domains of expertise, such as...SCRABBLE
(Tuffiash et al., 2007).”
-Ericsson et al. (2009, p. 9)
Duckworth et al. (2011)2“Our major findings in this investigation are as follows: Deliberate practice—operationally defined in the current
investigation as the solitary study of word spellings and origins—was a better predictor of National Spelling Bee
performance than either being quizzed by others or engaging in leisure reading. With each year of additional preparation,
spellers devoted an increasing proportion of their preparation time to deliberate practice...Grittier spellers engaged in
deliberate practice more so than their less gritty counterparts, and hours of deliberate practice fully mediated the
prospective association between grit and spelling performance.”
-Duckworth et al. (2011, p. 178)
In each quotation, the boldface emphasis on “deliberate practice” is added. 1Rejected because article “do[es] not record assigned individualized practice tasks with
immediate feedback and goals for practice” (see Ericsson, 2014b; Table 3). 2Rejected because article “do[es] not record a teacher or coach supervising and guiding all
or most of the practice” (see Ericsson, 2014b; Table 2). Table from Hambrick et al. (2018b); used with permission from John Wiley and Sons.
violating this same teacher/coach criterion. Just 2 years earlier, in
criticizing a journalist for his description of the study, Ericsson
(2012) emphatically stated that the study had collected data on
deliberate practice:
In that study we (as I was also one of the co-authors)
collected data on ‘deliberate practice.’ We found that
‘Grittier spellers engaged in deliberate practice more so
than their less gritty counterparts, and hours of deliberate
practice fully mediated the prospective association between
grit and spelling performance’ (p. 6).
In a commentary on a subsequent meta-analysis of deliberate
practice in sports performance, Ericsson (2016) again insisted
that our broad definition of deliberate practice was incorrect
(for a reply, see Macnamara et al., 2016a). Yet he did not
resolve or acknowledge the material inconsistencies in his past
descriptions of deliberate practice, especially those concerning
the important question of who designs deliberate practice
activities (see Figure 2). Furthermore, Ericsson again criticized
our inclusion of studies that he had previously used to argue for
the importance of deliberate practice (e.g., Helsen et al., 1998;
Hodges and Starkes, 1996). It is difficult, if not impossible, for
scientists to test a theory if the definition of and standard for
evidence are changed repeatedly, with no acknowledgment of and
no explanation for the changes (Ferguson and Heene, 2013).
New Types of Practice
Around this same time, in their aforementioned popular press
book Peak: Secrets from the New Science of Expertise, Ericsson and
Pool (2016) proposed a distinction between deliberate practice
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TABLE 2 | Ericsson’s evaluations of the 14 studies and associated effect sizes included in Ericsson and Harwell’s (2019) meta-analysis, from prior to 2014 to the present.
Study Evaluation by Ericsson prior to
Macnamara et al.’s (2014)
meta-analysis
Evaluation by Ericsson (2014a)
in commentary on
Macnamara et al. (2014)
Evaluation by Ericsson and
Harwell (2019) in their own
meta-analysis
Ericsson et al. (1993) – S1 Used to argue for the importance of DP Rejected for violating a criterion for DP1Coded as DP
Ericsson et al. (1993) – S2 Used to argue for the importance of DP Accepted as DP Coded as DP
Schultetus and Charness (1997)*** No cites to argue for importance of DP Rejected for violating a criterion for DP2Coded as DP
Baker et al. (2003) – DP2 No cites to argue for importance of DP Rejected for violating a criterion for DP2Coded as DP
Duffy et al. (2004) Used to argue for the importance of DP Rejected for violating a criterion for DP3Coded as PP
Charness et al. (2005) – S1 Used to argue for the importance of DP Rejected for violating a criterion for DP3Coded as DP
Reinterpreted as PP*
Charness et al. (2005) – S2 Used to argue for the importance of DP Rejected for violating a criterion for DP3Coded as DP
Reinterpreted as PP*
De Bruin et al. (2007)** Used to argue for the importance of DP Rejected for violating a criterion for DP3Coded as PP
Gobet and Campitelli (2007) No cites to argue for importance of DP Rejected for violating a criterion for DP3Coded as DP
Tuffiash et al. (2007) Used to argue for the importance of DP Rejected for violating a criterion for DP3Coded as PP
Reinterpreted as PP*
Ruthsatz et al. (2008) – S2 No cites to argue for the importance of DP Rejected for violating a criterion for DP1Coded as DP
Harris (2008) – M1 Dissertation on DP from Ericsson’s lab Rejected for violating a criterion for DP3Coded as PP
Duckworth et al. (2011) Used to argue for the importance of DP Rejected for violating a criterion for DP3Coded as PP
Reinterpreted as PP*
Maynard et al. (2014) NA; unpublished study Rejected because inadequate info. on study Coded as PP
DP, deliberate practice; PP, purposeful practice. Each study/effect size in red was rejected by Ericsson (2014a) for violating one of his criteria at that time for deliberate
practice (see Supplementary Tables in Ericsson, 2014b): 1“Restriction of range in attained performance and accumulated deliberate practice” (Ericsson, 2014c; Table 5).
2“Articles do not record assigned individualized practice tasks with immediate feedback and goals for practice” (Ericsson, 2014c; Table 3). 3“Articles do not record a
teacher or coach supervising and guiding all or most of the practice” (Ericsson, 2014b; Table 2). *See Moxley et al. (2019), for reinterpretation of studies as focusing on
purposeful practice rather than deliberate practice. **Ericsson and colleagues (see Ericsson and Towne, 2013) cite another report of this chess study by de Bruin and
colleagues to argue for the importance of deliberate practice (De Bruin et al., 2008), but that article is based on the same data as reported in the De Bruin et al. (2007)
article. ***As cited in Deakin and Cobley (2003).
and two new forms of practice, perhaps in an attempt to
address discrepancies and confusion surrounding the definition
of deliberate practice that were being documented in the scientific
literature (e.g., Hambrick et al., 2014a). They introduced and
defined naïve practice as “essentially just doing something
repeatedly, and expecting that the repetition alone will improve
one’s performance” (p. 14), and purposeful practice as an activity
that has well-defined, specific goals and involves feedback, but
which is self-directed rather than teacher-directed. Ericsson and
Pool (2016) explained that “deliberate practice requires a teacher
who can provide activities designed to help a student improve
his or her performance....With this definition we are drawing
a clear distinction between purposeful practice—in which a
person tries very hard to push himself or herself to improve—
and practice that is both purposeful and informed” (p. 98,
emphasis added). They further explained that “some approaches
to training are more effective than others” (p. 85) and that
deliberate practice is “the most effective method of all. . ..the gold
standard, the ideal to which anyone learning a skill should aspire”
(Ericsson and Pool, 2016, p. 85).
Using this new framework, Ericsson and colleagues
reinterpreted studies they had once used to argue for the
importance of “deliberate practice” as studies of the less effective
“purposeful practice,” but without explicitly acknowledging and
justifying the reinterpretation (see Macnamara and Hambrick,
2020). As an example, Ericsson (2005) described Charness
et al.’s (2005) chess study (entitled The Role of Deliberate
Practice in Chess Expertise) as providing “the most compelling
and detailed evidence for how designed training (deliberate
practice) is the crucial factor in developing expert chess
performance” (p. 237). Nevertheless, in their recent article,
Moxley et al. (2019) explained that “Charness et al. (2005)
found evidence for an independent effect of engagement in
purposeful practice for chess skill” (p. 1163, emphasis added).
As another example, Duckworth, Ericsson, and colleagues’
spelling bee study (Duckworth et al., 2011) focused on deliberate
practice: The article reporting the study was titled Deliberate
Practice Spells Success: Why Grittier Competitors Triumph at
the National Spelling Bee and the major conclusion of the
study was that “[d]eliberate practice mediated the prediction of
final performance by the personality trait of grit” (p. 174). Yet
the recent Moxley et al. (2019) article stated that “[a]fter the
questionnaire, we asked participants to fill out several additional
personality measures that Duckworth et al. (2011) had found to
be related to purposeful practice in preparation for competitions
in spelling” (p. 1158, emphasis added).
In another instance, Ericsson and colleagues went from
arguing that activities exist that meet the criteria for deliberate
practice in the boardgame SCRABBLE, to arguing that it is
not possible to engage in deliberate practice in SCRABBLE.
Specifically, referring to Tuffiash et al.’s (2007) SCRABBLE study,
Ericsson et al. (2009) stated that “[s]everal researchers have
reported a consistent association between the amount and quality
of solitary activities meeting the criteria of deliberate practice and
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performance in different domains of expertise, such as. . .Scrabble
(Tuffiash et al., 2007)” (p. 9). However, Moxley et al. (2019) wrote
that because SCRABBLE lacks professional coaches “SCRABBLE
players cannot engage in deliberate practice, but only purposeful
practice and other types of practice” (p. 1150). Under this new
framework, activities that once qualified as deliberate practice are
now classified as less effective purposeful practice. Of course, it is
appropriate for a theorist to reinterpret past evidence as a theory
is refined and revised over time. But it is a serious problem, as in
this case, when the reinterpretations of evidence are not explicitly
acknowledged, explained, and justified. In the absence of such
transparency, the reader will be led to think that the empirical
support for the original theory was stronger than it is or ever was.
Recent Developments
Two reanalyses of our first meta-analysis (Macnamara et al.,
2014) have been published in the past two years. The first was by
Miller et al. (2018), who argued that some of the studies that we
included in our meta-analysis did not capture deliberate practice.
Reanalyzing our data, Miller et al. (2018) explained that they had
raters code each study “using only the methods section” (p. 6)
and explained that “a study was coded as DP (deliberate practice)
if and only if it explicitly indicated it estimated the effects
of deliberate practice” (p. 6). Their meta-analysis revealed an
average correlation of r= 0.40 for “deliberate practice” (compared
to our value of r= 0.38), and an average correlation of r= 0.21
for what they deemed “non-deliberate practice.” However, as
we pointed out in a reply (Hambrick and Macnamara, 2019a;
see also Hambrick and Macnamara, in press), numerous studies
Miller et al. (2018) coded as deliberate practice did not meet
their own inclusion criteria. For example, Miller et al. (2018)
coded Bilali´
c et al.’s (2007) chess study as a deliberate practice
study. Yet Bilali´
c et al. (2007) made no mention of “deliberate
practice” anywhere in their methods, and elsewhere in their
article Bilali´
c et al. (see pp. 467–468) explicitly stated that they
did not interpret their practice measures as deliberate practice.
This study and numerous others fulfilled the broad definition of
deliberate practice we used in our own meta-analysis, but they did
not meet Miller et al.’s (2018) narrower definition. There are two
possibilities here: Miller et al. (2018) made errors in coding their
studies, or they used coding criteria different from those stated
in their article.
In a recent reply, while not addressing the coding issue, Miller
et al. (2019) highlighted that the average deliberate practice-
performance correlation in their reanalysis and in our meta-
analysis were very similar, which is an accurate observation
(r= 0.40 vs. r= 0.38). Then they noted, “Still, something about
our analyses [Miller et al.’s 2018, reanalysis] was displeasing to
[Hambrick and Macnamara, 2019a]” (Miller et al., 2019, p. 289).
We were actually clear about what the problem was: Miller
et al.’s (2018) meta-analysis included studies that clearly did not
meet their own stated inclusion criterion, rendering their results
uninterpretable. Miller et al. (2019) added, “The central question
of both studies was the role played by deliberate practice in the
acquisition of expertize [sic]. One might think there would be a
‘meeting of the minds’ when the estimates from their analysis and
ours returned such similar results” (p. 289). Miller et al. (2019)
seem to suggest here that because their reanalysis yielded an
average deliberate practice-performance correlation very similar
to the corresponding correlation in our meta-analysis, we should
have found their results acceptable. However, findings from
scientific research should be evaluated based on whether they
are accurate and interpretable, not on whether they agree with
findings from one’s own research. On that note, we reiterate that
until Miller and colleagues can clarify their methods the results
of their reanalysis will only add to the confusion surrounding
deliberate practice. As it stands, the results of their reanalysis
remain uninterpretable.
The second reanalysis of our dataset was by Ericsson and
Harwell (2019). Again, without resolving past inconsistencies in
descriptions of deliberate practice in Ericsson and colleagues’
writings, Ericsson and Harwell (2019) criticized our use of a
general definition of deliberate practice, stating:
There is no disagreement that the goal of improving
performance is one characteristic of deliberate practice, and
Ericsson et al. (1993) even wrote that “deliberate practice
is a highly structured activity, the explicit goal of which
is to improve performance” (p. 368). This sentence was,
however, not a definition of deliberate practice any more
than the true statement that “a dog is an animal” would
imply the inference that “all animals are dogs.” (p. 5).
According to this statement, Ericsson et al. (1993) never
proposed the general definition of deliberate practice that we
used for our meta-analysis. Yet, Lehmann and Ericsson (1997)
stated that “Ericsson et al. (1993) have defined deliberate practice
as a structured activity designed to improve performance” (p.
47, emphasis added). So, to be clear, Lehmann and Ericsson
(1997) stated that Ericsson et al. (1993) defined deliberate practice
as a structured activity designed to improve performance, but
then Ericsson and Harwell (2019) stated that Ericsson et al.
(1993) did not define deliberate practice as such. Contradictory
statements such as these lead to confusion about what the
definition of deliberate practice is—and even whether there is a
fixed definition at all.
Compounding the issues already discussed, Ericsson and
colleagues’ standard for evidence concerning deliberate practice
has apparently shifted yet again. For their main analysis, Ericsson
and Harwell (2019) retained 14 of the 88 studies included in
our meta-analysis (Macnamara et al., 2014), coding from these
studies eight effect sizes as deliberate practice and six effect
sizes as purposeful practice3. However, as shown in Table 2,
seven of the eight effect sizes they coded as deliberate practice
were from studies previously rejected by Ericsson (2014a) in his
commentary on Macnamara et al.’s meta-analysis for not meeting
the criteria for deliberate practice (recall that he rejected 87 of
the 88 studies in that commentary). The major reason for this
3Ericsson and Harwell (2019) state they coded eight effect sizes as deliberate
practice and six effect sizes as purposeful practice. They list the deliberate practice
effect sizes being from: Baker et al. (2003),Charness et al. (2005), both effect sizes;
Ericsson et al. (1993), both effect sizes; Schultetus and Charness (1997),Gobet and
Campitelli (2007), and Ruthsatz et al. (2008). And they list the purposeful practice
effect sizes being from: Duffy et al. (2004),Tuffiash et al. (2007),Harris (2008),
Duckworth et al. (2011), and Maynard et al. (2014). We assume that De Bruin et al.
(2007) was the sixth purposeful practice study and that its omission from the latter
list is a clerical error by Ericsson and Harwell (2019).
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discrepancy is that Ericsson and Harwell (2019) used a more
lenient teacher/coach criterion for deliberate practice in their
own meta-analysis than Ericsson (2014a) used when evaluating
our meta-analysis. More specifically, whereas Ericsson (2014a)
required that a study “record a teacher or coach supervising
and guiding all or most of the practice” (see Ericsson, 2014b,
Table 2), Ericsson and Harwell (2019) required only that a study
describe individualized sessions with a coach or teacher, with no
specification of the lower limit on the amount of supervision and
guidance. As they explained:
For coding purposeful versus deliberate practice, we looked
for explicit mentions within the original study methods of
individualized sessions with coaches or teachers as being
included as part of the estimate of solitary practice. If
a study did describe individualized instruction sessions
as being part of the practice estimate it was considered
to be deliberate practice (Ericsson and Harwell, 2019,
Supplementary Material, p. 5).
Ericsson and Harwell (2019) do not explain why they made
this shift from the stricter criterion that Ericsson (2014a)
used when evaluating our meta-analysis to a more lenient
teacher/coach criterion for themselves when they reanalyzed our
meta-analytic data. Interestingly, they do not even cite Ericsson’s
(2014a) commentary, although Ericsson (2018) very recently
used it as a basis for rejecting the conclusions of the Macnamara
et al. (2014) meta-analysis (see also Ericsson and Pool, 2016).
Additional examples of apparent shifts in the standard for
evidence concerning deliberate practice can be found in Ericsson
and Harwell (2019). For example, in his earlier commentary
on Macnamara et al. (2014),Ericsson (2014a) rejected Ruthsatz
et al.’s (2008) studies of musicians as a valid study of deliberate
practice because there was a restriction of range in the measures
(see Ericsson, 2014b, Table 5). However, Ericsson and Harwell
(2019) apparently no longer saw this as a problem, because
they coded one of these previously rejected studies (Study 2) as
deliberate practice and included it in their own meta-analysis.
Setting aside these issues, what would the results of Ericsson
and Harwell’s (2019) meta-analysis indicate if they were taken at
face value? After their correction for measurement error variance
(unreliability), the average correlation for the 14 studies that
measured either purposeful practice or deliberate practice with
performance increased from r= 0.54 to r= 0.78, indicating
that purposeful/deliberate practice4explained 61% of the reliable
variance in the performance. Although psychometric corrections
come with larger standard errors and confidence intervals
(Oswald et al., 2015), this point estimate of 61% might be
interpreted to support Ericsson et al.’s (1993) central claim
that individual differences in domain-relevant performance can
largely be accounted for by accumulated amount of practice—at
least a weak version of this claim (see Hambrick et al., 2018a).
However, it is critical to examine Ericsson and Harwell’s (2019)
assumptions about the reliability of the variables, because those
4Following Ericsson and Harwell’s (2019) terminology, when referring to all 14
effect sizes (six coded as purposeful practice and eight coded as deliberate practice)
in the meta-analysis, we use the label “purposeful/deliberate practice.”
values determined the degree of correction to the correlation (i.e.,
the lower the assumed reliability, the greater the correction). As
previously noted, Ericsson and colleagues previously stated that
“self-report practice estimates repeatedly from experts in sports
and music have reported test-retest reliabilities at or above 0.80”
(Tuffiash et al., 2007, p. 129) and that “[t]he collected reliability
of cumulated life-time practice at different test occasions in
large samples has typically been found to range between
0.7 and 0.8” (Ericsson, 2012, p. 534). However, for reasons
that are unclear, Ericsson and Harwell (2019) used a lower
reliability estimate of 0.60 for purposeful/deliberate practice,
apparently drawing on different information about the reliability
of practice. This raises the important question of whether, by
using a lower reliability value, they overcorrected the correlation
between purposeful/deliberate practice and performance, and
thus overestimated the variance shared between the two variables.
As is the case for most measured variables in psychological
research, the reality is that the reliability of practice and
performance variables must be estimated and can never be known
with certainty; the accuracy of any reliability estimate will vary
with the number of participants, the number of items/questions
on the instrument, and other factors. Furthermore, the reliability
of practice and performance variables may vary depending on
factors such as the domain, the skill level and age of the
participants, and so on. Thus, an approach we have adopted
in our own research is to correct correlations between these
variables, based on whatever point estimates of reliability are
available, but also to report a sensitivity analysis in which the
correlation is corrected under both lower and higher levels of
reliability (e.g., Macnamara et al., 2014, Table S1).
Table 3 presents such a sensitivity analysis for the meta-
analytic correlations that Ericsson and Harwell (2019) report
between purposeful/deliberate practice and performance
(r= 0.54, k= 14), between purposeful practice and performance
(r= 0.51, k= 6), and between deliberate practice and performance
(r= 0.56, k= 8). As can be seen, the strength of the correlations
varies depending on the reliability estimates, with larger
corrections for lower reliability estimates. For example, if
reliability is assumed to be rxx = 0.80 for deliberate practice
(Ericsson, 2013) and ryy = 0.80 for performance, then the
corrected correlation of performance with deliberate practice
is rc= 0.70, indicating that deliberate practice explains 49% of
the reliable between-person variance in performance (rather
than 61%). Thus, conclusions will vary considerably depending
on what values are used as the reliability estimates in the
psychometric correction.
Across the board, the correlations between practice and
domain-relevant performance (expertise) in Table 3 are
meaningfully large, from theoretical, statistical, and practical
perspectives. At the same time, the correlations vary considerably
in magnitude and may lead to different conclusions about the
importance of the practice variables. We illustrate this point with
reference to the deliberate practice correlations (the bottom set of
results in Table 3). If deliberate practice explained, for example,
87% of the variance in performance (assuming relatively low
reliabilities of rxx = 0.60 and ryy = 0.60), then a strong version
of Ericsson et al.’s (1993) claim that individual differences in
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TABLE 3 | Sensitivity analyses for Ericsson and Harwell (2019) meta-analytic correlations.
Performance ryy
Purposeful/Deliberate rxx 0.60 0.70 0.80 0.90
0.60 0.90 (81.0%) 0.83 (69.4%) 0.78 (60.8%) 0.73 (54.0%)
0.70 0.83 (69.4%) 0.77 (59.5%) 0.72 (52.1%) 0.68 (46.3%)
0.80 0.78 (60.8%) 0.72 (52.1%) 0.68 (45.6%) 0.64 (40.5%)
0.90 0.73 (54.0%) 0.68 (46.3%) 0.64 (40.5%) 0.60 (36.0%)
Performance ryy
Purposeful rxx 0.60 0.70 0.80 0.90
0.60 0.85 (72.3%) 0.79 (61.9%) 0.74 (54.2%) 0.69 (48.2%)
0.70 0.79 (61.9%) 0.73 (53.1%) 0.68 (46.4%) 0.64 (41.3%)
0.80 0.74 (54.2%) 0.68 (46.4%) 0.64 (40.6%) 0.60 (36.1%)
0.90 0.69 (48.2%) 0.64 (41.3%) 0.60 (36.1%) 0.57 (32.1%)
Performance ryy
Deliberate rxx 0.60 0.70 0.80 0.90
0.60 0.93 (87.1%) 0.86 (74.7%) 0.81 (65.3%) 0.76 (58.1%)
0.70 0.86 (74.7%) 0.80 (64.0%) 0.75 (56.0%) 0.71 (49.8%)
0.80 0.81 (65.3%) 0.75 (56.0%) 0.70 (49.0%) 0.66 (43.6%)
0.90 0.76 (58.1%) 0.71 (49.8%) 0.66 (43.6%) 0.62 (38.7%)
The table provides corrected correlation coefficients (rcs) based on Ericsson and Harwell’s (2019) reported meta-analytic correlations of rpurposeful/deliberate = 0.54,
rpurposeful = 0.51, and rdeliberate = 0.56. Values in parentheses are variance estimates (i.e., rc2×100). rxx, reliability estimate for practice; ryy, reliability estimate
for performance; DP, deliberate practice. The bolded values are the corrected correlations using Ericsson and Harwell’s (2019) reliability values. The formula for
computing a correlation corrected for unreliability (rc) is the observed correlation (robs) divided by the square root of the product of the variables’ reliabilities (rxx and
ryy): rc= robs /√(rxx ×ryy) (see Schmidt and Hunter, 1999).
performance can largely be accounted for by deliberate practice
would be supported. However, if deliberate practice explained
just over half (56%) of the variance (assuming higher reliabilities
of rxx = 0.80 and ryy = 0.70), then a weaker version of the claim
would be supported. If deliberate practice explained an even
smaller amount of the variance—for example, 39% (assuming
even higher reliabilities of rxx = 0.90 and ryy = 0.90)—then an
implication would be that factors other than deliberate practice
explain more of the variance in performance than deliberate
practice does. These different variance estimates might then lead
to different priorities in expertise research. For example, the first
scenario (87% of variance explained) might prompt an exclusive
focus on training history, whereas the third scenario (39% of
variance explained) might prompt a broader focus on multiple
determinants of performance differences (e.g., training history,
basic abilities). Elsewhere, we have argued that based on extant
evidence, research should indeed investigate the role of a wide
range of factors in explaining individual differences in expertise
(Ullén et al., 2016;Hambrick et al., 2018b).
We further note that, if they were taken at face value, Ericsson
and Harwell’s (2019) findings would fail to support a central
prediction of the latest version of the deliberate practice view.
As mentioned earlier, Ericsson and Pool (2016) differentiated
deliberate practice from naïve practice and purposeful practice,
describing deliberate practice as “the most effective method of
all. . .the gold standard” (p. 85). A straightforward prediction,
following from these claims, is that the positive correlation
between deliberate practice and domain-relevant performance
should be significantly greater than the positive correlation
between purposeful practice and domain-relevant performance
(i.e., rdeliberate >rpurposeful ). Ericsson and Harwell’s (2019)
findings do not support this prediction: the average correlation
was r= 0.56 for deliberate practice and r= 0.51 for purposeful
practice, a non-significant difference (p= 0.64). Ericsson and
Harwell (2019) report this finding, but they note only that
“practice was positively associated with performance whether it
was conducted under the guidance of a coach or teacher” (p.
11). From the standpoint of the distinction between the two
types of practice (Ericsson and Pool, 2016), the equally important
conclusion would seem to be that there is no evidence from
the meta-analysis that deliberate practice has higher validity
than purposeful practice in predicting individual differences in
domain-relevant performance, as is predicted by Ericsson and
colleagues’ new framework.
HOW IMPORTANT IS DELIBERATE
PRACTICE?
Again, how important is deliberate practice as a predictor
of individual differences in expertise? It is somewhat difficult
to say given the ambiguity over the definition of deliberate
practice, but we can at least summarize evidence from meta-
analyses by different groups of researchers that have attempted to
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answer this question. Along with the aforementioned reanalysis
of chess and music studies (Hambrick et al., 2014b), there
have been five formal meta-analyses of the deliberate practice-
performance relationship. Table 4 summarizes the overall result
from these meta-analyses (i.e., the overall correlation between
deliberate practice and performance), and presents a sensitivity
analysis showing variance estimates under different reliability
assumptions, from unacceptable to excellent. As shown, across
meta-analyses, deliberate practice explains a sizeable amount
of the between-person variance in performance. However, we
conclude that it is unlikely to be as important as Ericsson and
colleagues have hypothesized it is. In nearly all cases, deliberate
practice leaves a large amount of reliable variance unexplained,
and in most cases, the unexplained variance exceeds the explained
variance. Our conclusion, as in the past, is that deliberate practice
is an important predictor of individual differences in expertise.
However, deliberate practice is it is unlikely to be as important as
Ericsson and colleagues have proposed it is.
IS THE DELIBERATE PRACTICE VIEW
DEFENSIBLE?
In view of the issues discussed in the preceding pages, it
seems reasonable to ask whether the deliberate practice view is
scientifically defensible—that is, whether deliberate practice can
be conceptualized and tested empirically in a consistent manner
by the research community. In his book The Logic of Scientific
Discovery, the philosopher of science Karl Popper (1959) argued,
“In so far as a scientific statement speaks about reality, it must be
falsifiable; and in so far as it is not falsifiable, it does not speak
about reality” (p. 314). By definition, a theory is unfalsifiable
when it cannot be rejected under any circumstances, because
it can accommodate any finding. This happens when multiple,
contradictory definitions of theoretical concepts are proposed by
a theorist, and when the theoretical and operational criteria are
kept in a fluid state. Under these conditions, evidence can be
rejected or accepted depending on whether it supports the theory.
When a theory becomes unfalsifiable, it ceases to be a scientific
theory, at least in the Popperian sense. The theory is always
“right” and cannot be evaluated against competing theories.
Ferguson and Heene (2013) described a theory that has entered
this state as being “undead,” like a zombie that is technically
dead but remains animate. The undead theory “continues in use,
having resisted attempts at falsification, ignored disconfirmatory
data, negated failed replications through the dubious use of
meta-analysis or having simply maintained itself in a fluid state
with shifting implicit assumptions such that falsification is not
possible” (Ferguson and Heene, 2013, p. 559).
Is the deliberate practice view falsifiable? We will leave it to
readers of this article to draw their own conclusions. At the very
least, it seems difficult to deny that there are serious problems
with the deliberate practice view, as Ericsson and colleagues have
presented it over the past 25 years. As we have documented here,
Ericsson and colleagues have described deliberate practice in
contradictory ways, creating major confusion about the definition
of deliberate practice. Furthermore, Ericsson and colleagues’
standard for evidence—the specific criteria that need to be
satisfied to use a study to argue for the importance of deliberate
practice and even the criteria themselves—has appeared to
shift multiple times.
To be sure, it is not only normal, but expected, for
a scientist to revise a theory as evidence relevant to that
theory accumulates through research. Theory revision is a
fundamental part of what the philosopher of science Imre
Lakatos (1978) called a “progressive” program of research.
In this iterative process, revisions are explicitly acknowledged
and clearly explained and justified so that they can be
understood and critically evaluated by other scientists. When
theory revisions are not made in a transparent manner, then
in Lakatos’ terminology a theory can be endlessly adjusted
and readjusted to keep it “alive.” The program of research
then shifts from “progressive” to “degenerative” (see also
Musgrave and Pigden, 2016).
On a related note, the concept of deliberate practice is
arguably underspecified in ways that leave open the opportunity
for numerous post hoc explanations of results. For example,
Ericsson and colleagues have stated that deliberate practice
requires that the performer have “full concentration” (e.g.,
Ericsson and Harwell, 2019). However, this is a psychological
state that may be impossible to achieve. Would, for example, a
person’s awareness of the environment (e.g., the temperature)
or a fleeting thought (e.g., about an event earlier in the day)
mean that they were not fully concentrating on the training
task? If yes, then it seems unlikely that a person could
ever fulfill this criterion of deliberate practice. Furthermore,
we are not aware of a method for objectively determining
whether a person has full concentration on something. Research
approaches from cognitive psychology (e.g., primary-secondary
task paradigms) permit no more than relative statements about
the degree to which a person is attending to one task (or
stimulus) versus some other task(s). Except for subjective
self-reports, we are also unaware of attempts by Ericsson
and colleagues themselves to measure concentration level
during practice. If a criterion for a theoretical construct (e.g.,
achieving “full concentration”) either cannot be achieved or
cannot be empirically verified, then imposing that criterion
makes the theory unfalsifiable. This sort of flexibility in the
deliberate practice view, along with the definitional confusion
we have discussed, presents an additional problem for its
scientific viability.
Why is it important for researchers to comment publicly
when a theory in their research area appears to be degenerating?
It is important because a degenerating theory—especially if it
is influential—impedes actual progress in an area of research.
The value of research to test the theory becomes questionable,
because evidence is accepted as valid only if it supports
the theory and rejected if it fails to support the theory. In
turn, practical recommendations and applications based on
the theory will lack a scientific foundation, because even
conflicting recommendations and applications can be supported.
If conclusions from this research do not have a solid empirical
foundation, then recommendations based on the theory may be
wasteful, counterproductive, or even harmful.
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TABLE 4 | Results of meta-analyses of deliberate practice–performance relationship.
Study Domain K r (95% CI) %
variance
<Acceptable
(rxx,ryy = 0.60)
Acceptable
(rxx,ryy = 0.70)
Good
(rxx,ryy = 0.80)
Excellent
(rxx,ryy = 0.90)
Macnamara et al. (2014) Multiple 157 0.38 (0.33, 0.42) 14.4% 0.63 (40.1%) 0.54 (29.5%) 0.48 (22.6%) 0.42 (17.8%)
Platz et al. (2014) Music 14 0.61 (0.54, 0.67) 37.2% 1.00 (100%) 0.87 (75.9%) 0.76 (58.1%) 0.68 (45.9%)
Macnamara et al. (2016b) Sports 63 0.45 (0.36, 0.53) 20.3% 0.75 (56.3%) 0.64 (41.3%) 0.56 (31.6%) 0.50 (25%)
Miller et al. (2019) Multiple 70 0.40 (0.34, 0.46) 16.0% 0.67 (44.4%) 0.57 (32.7%) 0.50 (25%) 0.44 (19.8%)
Ericsson and Harwell (2019) Multiple 8 0.56 NR 31.4% 0.93 (87.1%) 0.80 (64.0%) 0.70 (49%) 0.62 (38.7%)
k, number of effect sizes. r, meta-analytic correlation between deliberate practice and performance.% variance, percentage of variance in performance explained by
deliberate practice (i.e., r2). For the corrected correlations, values in parentheses adjacent to correlations are variance estimates (rc2×100). NR, not reported.
To be certain, we do not think that the concept of deliberate
practice should be abandoned. Deliberate practice is a vital area
of research in psychological science and other fields. However,
given the issues we have discussed at length in this article,
we do believe that it would be highly beneficial to expertise
researchers (and scientists from other research areas interested
in expertise) for proponents of the deliberate practice view to
fully address and resolve the apparent inconsistencies and shifts
in the definition of and standard of evidence for deliberate
practice that we have documented here—and which raise serious
concerns about the viability of the deliberate practice view
as a scientific theory. This would allow other researchers to
empirically evaluate the importance of deliberate practice as a
predictor of individual differences in expertise, both in individual
studies and meta-analyses, and to compare its predictive validity
to that of other factors (e.g., general aptitudes, basic capacities)
and the conditions of practice (e.g., spacing of practice sessions,
type of feedback).
To this point, in the following sections, we summarize key
evidence for the role of a diverse range of factors in explaining
individual differences in expertise, and then discuss in broad
terms what we believe are fruitful directions for future research
to develop comprehensive models of expertise.
TOWARD A MULTIFACTORIAL MODEL
OF EXPERTISE
What might explain individual differences in expertise, beyond
any contribution of deliberate practice? We direct readers to
recent theoretical/review articles in which we discuss this issue
at length (Hambrick et al., 2016;Ullén et al., 2016;Macnamara
and Hambrick, 2020). Here, we briefly summarize evidence
concerning three major classes of factors.
Developmental Factors
The question of when specialized training should commence
in a person’s life is the subject of a longstanding debate in
the field of expertise. The early specialization view argues that
the earlier the training can begin, the better. The logic of
this view is straightforward: Because it is both physically and
psychologically taxing, a person can engage in only a few hours
of deliberate practice a day (around 4 h on average; Ericsson
et al., 1993) without burnout and/or injury. Therefore, the
individual who begins training at a relatively late age (e.g.,
age 12) can never catch up to the individual who begins
training earlier (e.g., age 6). However, in a meta-analysis of
sports studies with samples representing a wide range of skill,
we found no evidence for an earlier average starting age for
high-skill athletes relative to lower-skill athletes. Furthermore,
research suggests that the highest (elite) levels of sports
performance are associated with a later starting age, combined
with participation in a diverse range of sports in adolescence.
For example, Güllich (2017) compared 83 international medalists
(Olympic/World championship) to 83 non-medalists matched
on sport, age, and gender. Up to age 18, the medalists had,
on average, accumulated significantly fewer hours of organized
training/practice in their main sport (by 948 h) than the non-
medalists. Moreover, the average starting age was later by
approximately a year-and-a-half for the medalists compared to
the non-medalists. One possible explanation for this finding
is that a starting age that is too early increases the risk for
injury and/or burnout. Another possible explanation is that
starting later allows for more early diverse experiences, increasing
the likelihood that the individual will find a sport that is a
good match to his or her profile of performance-relevant traits
(Güllich, 2017).
Experiential Factors
A central tenet of the deliberate practice view is that
deliberate practice is more predictive of individual differences
in expertise than other forms of experience, such as work
and play. As Boot and Ericsson (2013) explained, “Ericsson
and colleagues. . .make a critical distinction between domain-
related activities of work, play, and deliberate practice, and claim
that the amount of accumulated time engaged in deliberate
practice activities is the primary predictor of exceptional
performance” (p. 146). The available evidence does not appear
to support this claim. As already mentioned, if Ericsson
and Harwell’s (2019) findings are taken at face value, they
reveal that deliberate practice, although claimed to be the
“gold standard” for improving performance (Ericsson and Pool,
2016), is not a significantly stronger predictor of individual
differences in expertise than mere purposeful practice (i.e.,
rdeliberate = 0.56 vs. rpurposeful = 0.51, difference in rs non-
significant). Furthermore, measures of deliberate practice have
not always been found to be stronger predictors of individual
differences in expertise than measures seeming to meet the
definition of “work” and “play.” For example, in a study
of insurance salespeople, Sonnentag and Kleine (2000) found
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that correlations between sales performance and measures of
deliberate practice (rcurrent = 0.21 and raccumulated = 0.13) were
not stronger than the correlation between sales performance and
a measure fitting the description of work for this domain—the
number of cases handled (r= 0.37; see Hambrick et al., 2016, for
other examples).
Evidence further suggests that diverse forms of experience
are important as well, especially in the early stages of training.
For example, in Güllich’s (2017) study comparing the 83
international medalists and 83 non-medalists, he not only
found that the medalists had accumulated significantly less
main-sport practice than their less-accomplished counterparts
during childhood/adolescence, but also that the medalists had
accumulated significantly more experience with other sports
during this period (see also Güllich et al., 2017).
Ability Factors
Research has firmly established that cognitive ability explains a
statistically and practically significant amount of the variability
in people’s acquisition of complex skills (Hambrick et al., 2019;
also see Ackerman, 1987, for a review of early studies). That
is, people higher in cognitive ability learn complex skills more
readily and rapidly than people lower in cognitive ability. For
example, in a study of music training, participants with little or
no experience playing music completed tests of cognitive ability,
music aptitude, and growth mindset, and then they were given
instruction in playing a simple piece of music on the piano
(Burgoyne et al., 2019). Higher-ability participants showed a
greater rate of learning than lower-ability participants, with a
general intelligence factor explaining approximately 30% of the
individual differences in learning rate.
Ericsson (2014d) has theorized that general cognitive ability is
important initially in acquiring complex skills, but its predictive
power diminishes as domain-specific skills and knowledge are
acquired, stating:
For individuals who have acquired cognitive structures that
support a high level of performance the expert performance
framework predicts that these acquired cognitive structures
will directly mediate superior performance and thus
diminishing correlations between general cognitive ability
and domain-specific performance (p. 84).
For complex tasks of interest to expertise researchers, evidence
for this claim, which we termed the circumvention-of-limits
hypothesis (Hambrick and Meinz, 2011), is weak and inconsistent.
In a recent review (Hambrick et al., 2019), we searched through
approximately 1,300 articles and identified 15 studies in the
domains of games, music, sports, science, medicine/surgery, and
aviation relevant to this hypothesis. Of the 15 studies, only three
yielded any evidence supportive of the circumvention-of-limits
hypothesis. Moreover, methodological limitations (e.g., small Ns,
measures with unknown or unreported reliability) precluded any
strong conclusions from those few studies. Providing what might
be considered the strongest evidence for the hypothesis, one of
these three studies that seem to support the circumvention-of-
limits hypothesis was a meta-analysis of chess studies (Burgoyne
et al., 2016; see also Burgoyne et al., 2018, corrigendum). As
determined by a moderator test, fluid intelligence correlated
significantly more strongly with chess rating in lower-skill chess
players (avg. r= 0.32) than in higher-skill chess players (avg.
r= 0.14). However, it is important to note that skill level was
highly confounded with age (i.e., lower-ability samples were
youth, whereas higher-ability samples were adults), limiting the
strength of the evidence in support of the circumvention-of-
limits hypothesis.
We also note that results that have sometimes been used
to argue that the influence of general cognitive ability on
expertise diminishes with increasing skill do not warrant this
conclusion. For example, Ericsson (2014d) pointed to results by
Ruthsatz et al. (2008) as support for this hypothesis. Ruthsatz
et al. (2008) found that a measure of general cognitive ability
(Raven’s Progressive Matrices score) correlated positively and
significantly with musical accomplishment in high school band
members (r= 0.25, p<0.05), but not in university music majors
(r= 0.24) or conservatory students (r= 0.12). However, the
critical question is not whether the lower-skill group correlation
is statistically significant while the higher-skill group correlations
are not. Rather, it is whether the former correlation and the
latter correlations are significantly different from each other, as
determined by the appropriate statistical test. As it happens,
in the Ruthsatz et al. (2008) study, the correlations are not
significantly different from each other (all ztests for differences
in correlations are statistically non-significant). Thus, the results
of Ruthsatz et al.’s (2008) study fail to support the hypothesis that
ability-performance correlations diminish with increasing skill.
We also reviewed evidence relevant to the circumvention-
of-limits hypothesis from the job performance literature, and
here the evidence is more consistent and interpretable. General
cognitive ability is regarded as the single best predictor of
job training performance, and of subsequent job performance
(Schmidt and Hunter, 2004;Schmidt, 2014). Higher ability
people tend to learn job skills more rapidly and to a higher level
than lower ability people, and in turn have greater success on
the job. Furthermore, although the validity of general cognitive
ability for job performance may drop somewhat as a function
of job experience initially, it appears to remain a statistically
significant predictor even at high levels of job experience. For
example, in a study of 10,088 military personnel across 31 jobs,
the correlation between a measure of general cognitive ability
(the AFQT score) and hands-on job performance decreased
as a function of job experience from 1 to 2 years of job
experience (r= 0.34 to r= 0.21; ztest for difference = 3.60,
p<0.001), but then stabilized and remained statistically and
practically significant (see Figure 3). In their own review of the
job performance literature, Reeve and Bonaccio (2011) concluded
that “although validities might degrade somewhat over long
intervals, we found no evidence to suggest that they degrade
appreciably, thereby retaining practically useful levels of validity
over very long intervals” (p. 269).
Genetic and Environmental Influences
Research in the field of behavioral genetics has demonstrated
that both genetic and environmental variance across individuals
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contribute to the total variance in a wide range of behavioral
outcomes (Turkheimer, 2000), including ability factors that
have been found to correlate with measures of expertise. The
extent of the genetic contribution is captured by the heritability
statistic (h2), an estimate of the proportion (0 to 1) of the
total variance in a trait that can be attributed to genetic (non-
environmental) variance within a sample of individuals (Plomin
et al., 2008). Because some of these factors correlate with
expertise, it stands to reason that both genetic and environmental
variance may also contribute to the total variance in expertise.
Furthermore, basic abilities and characteristics that may predict
individual differences in expertise have also been observed to be
substantially heritable, including drawing ability (Arden et al.,
2014), music aptitude (Ullén et al., 2014; see Mosing et al.,
2018, for a review), and maximal oxygen consumption in athletic
performance (VO2max;Schutte et al., 2016).
At the same time, no psychological trait is 100% heritable
(Turkheimer, 2000), and even the most heritable psychological
trait will have a sizeable environmental component. For example,
heritability estimates for measures of general cognitive ability
are typically in the 50 to 70% range in samples drawn from
developed countries (e.g., Tucker-Drob and Bates, 2016), with the
remaining variance (as much as 50%) explained by shared and/or
non-shared environmental factors. This means that correlations
between a measure of some trait (e.g., general cognitive ability)
and a measure of expertise could be driven by the genetic variance
or the environmental variance in the trait measure, or by both
FIGURE 3 | Correlation between the Armed Forces Qualifying Test (AFQT)
score and hands-on job performance (HOJP) for 31 military jobs (N= 10,088).
(A) shows AFQT-HOJP correlations when four levels of job experience are
created; (B) shows this correlation when eight levels of job experience are
created. Figure from Hambrick et al. (2019); used with permission of Oxford
University Press.
types of variances. In other words, the finding that a measure of
a heritable trait correlates with expertise is only consistent with
the possibility that genetic variance is a component of individual
differences in expertise.
It is also critical to note that genes and environments cannot
generally be assumed to be uncorrelated across people. Rather,
across people, genetically influenced factors may contribute to
variance in the environments which people seek out and are
exposed to. This is the idea of gene-environment correlation, or
rGE (Plomin et al., 1977). For example, just as children who are
tall might be more interested in playing basketball and more
likely to be selected to play on basketball teams than children
who are shorter, those with a high level of music aptitude may
be more likely to take up, be selected for, and persist in music
than those with a lower level of this aptitude. Consistent with
this sort of speculation, there is now evidence to indicate that the
propensity to practice in a domain is substantially heritable. In a
large twin study, Mosing, Ullén, and colleagues found an average
heritability of around 50% for accumulated amount of music
practice (Mosing et al., 2014; see also Hambrick and Tucker-
Drob, 2015). A possible explanation for this finding is that music
aptitude, as well as more general ability and non-ability factors,
differentially predispose people to engaging in music practice.
Genetic factors and environmental factors may not only
correlate with one another; they may interact in influencing
behavioral outcomes—what is known as gene-environment
interaction, or G ×E. G ×E occurs when a genetically
influenced factor moderates (increases or decreases) the effect
of an environmental factor on an outcome. As one example of
G×E, analyzing data from the National Merit Twin Study,
Hambrick and Tucker-Drob (2015) found that heritability of a
music accomplishment variable was 0.43 for individuals who
reported engaging in music practice, versus 0.01 for those who
did not. (This result was not due to range restriction, as there was
still variability in music accomplishment among participants who
reported not practicing). This finding suggests that music practice
may activate genetic factors that vary across people.
Four additional points concerning the potential contribution
of genetic factors to individual differences in expertise are
important to note here. First, even if a measure of expertise
is found to be heritable, this in no way implies that training
is unnecessary to develop a high level of expertise, or that
training is beneficial to only some people. Training is necessary
and essential for developing a high level of expertise in a
domain, and except when a condition rules out some type
of training (e.g., a visual training regimen for a person who
is blind), anyone would be expected to benefit from proper
training. Second, heritability does not imply immutability. For
example, in adults, height is highly heritable and relatively
fixed, whereas weight is similarly heritable but can be modified
through an environmental intervention—namely, dieting. Third,
environmental interventions that change individual differences
will also change heritability. For example, if an environmental
intervention were introduced that allowed nearly everyone to
reach about the same level of skill in some task, heritability
would be expected to decrease. In a similar manner, heritability
can differ across populations (e.g., in a developing country
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vs. a non-developing country; Tucker-Drob and Bates, 2016).
Fourth, and finally, it is safe to assume that to the degree that
expertise is heritable, this would reflect variation in a great
many genetic variants (i.e., single nucleotide polymorphisms, or
SNPs), meaning there is no “expertise gene” in any domain. As
Chabris and colleagues noted, “A typical human behavioral trait
is associated with very many genetic variants, each of which
accounts for a very small percentage of the behavioral variability”
(Chabris et al., 2015, p. 305). Research is uncovering genetic
variants that may contribute to individual differences in expertise,
but it is highly unrealistic to expect that any one of these factors
will account for a large amount of the variance in expertise.
Putting It All Together
Taken together, evidence suggests that individual differences in
expertise arise from influences of multiple factors. This includes
training and other forms of domain-relevant experience, as well
as developmental factors (e.g., age of starting training), ability
factors (e.g., aptitudes), non-ability factors (e.g., personality
traits), and background factors (e.g., opportunity to engage in
training). In recent articles, we have proposed the multifactorial
gene-environment interaction model (MGIM) to describe how
these factors relate to one another and to serve as a guide for
future research on expertise (Ullén et al., 2016). As illustrated in
Figure 4, the MGIM assumes that expertise arises from influences
of both domain-general and domain-specific factors, which are
assumed to be influenced by both genetic and environmental
factors. The model further assumes that task/situational factors
may moderate the influence of these factors on expertise (i.e.,
domain-relevant performance).
THE PATH AHEAD IN EXPERTISE
RESEARCH
Ericsson and colleagues’ deliberate practice view has had a
monumental impact on the field of expertise, and is important
in the history of psychology, more generally. However, in
our assessment, it is not clear that the deliberate practice
view is defensible as a scientific theory. As described here in
some detail, Ericsson and colleagues have defined deliberate
practice in inconsistent ways (see Figure 2) and the standard
for evidence concerning deliberate practice has appeared to
shift multiple times (see Tables 1,2). These issues present
problems for the empirical testability (i.e., falsifiability) of the
deliberate practice view.
Embracing tenets of the open science movement, we believe
that the path ahead is for expertise researchers to work
together to develop testable theories that take into account
a much wider range of potentially relevant causal constructs
than have often been considered in previous research, and
to use rigorous empirical methods to evaluate these theories.
The open science movement promotes normative values aimed
at increasing accuracy, openness, and fairness in scientific
research and scholarship (see a special issue of the Journal
of Expertise devoted to open science in expertise research;
McAbee and Macnamara, 2020).
Nearly 80 years ago, the sociologist of science Robert Merton
(1942) described four scientific “imperatives” that capture the
values of the open science movement. First, universalism:
scientific validity is independent of the status of the people
conducting the research; evidence should be evaluated based
on its own merits rather than the status or prominence of the
person reporting the evidence. Second, communism: a theory
does not belong to the theorist, it belongs to the field. The
theorist has no greater right to the theory once it is made
public than any other scientist. Third, disinterestedness: scientists
should perform research to increase understanding of some
phenomenon rather to advance self-interests, whatever they may
be. Finally, organized skepticism: a field should scrutinize claims
based on empirical evidence.
In the wake of the replication crisis in the social sciences, many
measures have been proposed to increase the reproducibility
of research findings in psychological science and to accelerate
progress in research (see Munafò et al., 2017). Preregistration
of study designs, primary outcomes, and data analysis plans can
help safeguard against post hoc interpretation of data. Improved
methodological training can help researchers avoid pitfalls in
designing studies (e.g., omitting critical control conditions) and
in data analysis (e.g., misinterpreting p-values). Collaboration
can facilitate collection of large samples and help to ensure
that multiple theoretical perspectives are considered in study
design. The time is also ripe for a preregistered “adversarial
collaboration”—a study in which researchers with differing views
agree on an empirical test to resolve a theoretical dispute that is
designed to provide a fair test of both views (see Kahneman and
Klein, 2009; for a recent example, see Doherty et al., 2019). No less
so than in any area of psychological research, we believe that open
science will accelerate progress toward greater understanding of
the nature and origins of expertise and expert performance.
Recommendations for Expertise
Research
We make four general recommendations for conducting
expertise research, which are based on best research practices
in differential psychology (see Ackerman and Hambrick, 2020).
First, after selecting a domain for the research, the researchers
should seek to assess a wide range of potentially relevant causal
factors (Simonton, 1999). Whatever the domain, it will be critical
to measure key environmental factors, including various types of
training and factors related to the opportunity to engage in these
activities. However, drawing on the vast literature in differential
psychology, it will be equally critical to include measures of
basic abilities, capacities, dispositions, and other psychological
traits that may affect performance directly, or indirectly through
training. Only then can the relative and joint contributions
of these factors to individual differences in expertise
be evaluated.
The second recommendation is that multiple measures be
used to index each of the hypothesized constructs. It is axiomatic
in the psychological methods literature that virtually no observed
measure (or indicator) is “construct pure.” That is, a score
collected by an instrument (test, questionnaire, etc.) designed
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FIGURE 4 | Multifactorial model of expertise. From Hambrick et al. (2018c). Used with permission of Routledge.
to measure a given hypothetical construct may reflect that
construct to some degree, but it will certainly reflect other,
construct-irrelevant factors, such as participants’ familiarity
with a particular method of assessment (e.g., test format) and
psychological states that may affect their responding (e.g., sleep
deprivation and motivation). There is no perfect way to deal
with this problem, but when multiple measures of a construct
are obtained, it becomes possible to use data-analytic techniques
(viz., structural equation modeling) that are explicitly designed
to deal with this issue by allowing researchers to model latent
variables that are closer to theoretical constructs of interest than
observed variables are.
The third recommendation is that the sample of participants
from the targeted domain should ideally represent a wide range
of performance rather than extreme groups. As we have noted
elsewhere (Hambrick et al., 2019), categories such as “novice”
and “expert” are not naturally occurring—they are groups
of performers created based on ultimately arbitrary cuts on
performance scores. Accordingly, scientific research on expertise
should endeavor to explain the full range of performance
differences within different domains rather than differences
between artificial groups of performers, and also continuities and
discontinuities across this range (see Bliese and Lang, 2016).
The final recommendation is for expertise researchers to
begin large-scale longitudinal studies. Longitudinal studies are
expensive and, by their very nature, time-consuming. At the
same time, they are common in psychology. For example,
there are longitudinal studies in the area of cognitive aging
that have been running for many decades, such as the Seattle
Longitudinal Study (Schaie, 2005). There is also precedent
for longitudinal studies of expertise, including Schneider and
colleagues’ important longitudinal study of tennis skill, which
included two future World No. 1 players (Schneider et al.,
1993). Although expensive, labor-intensive, and time-consuming,
multi-site longitudinal studies of expertise will provide for much
stronger conclusions concerning the underpinnings of expertise.
We also offer one more specific recommendation for future
research on expertise. The goal of Friedlander and Fine’s (2016)
grounded expertise components approach (GECA) is to identify
predictors of individual differences in expertise in a theoretically
neutral manner to minimize bias in findings from research
concerning the relative importance of one class of factor versus
another (e.g., training vs. basic abilities). The approach begins
with exploration: administering a survey to a large sample of
performers in domains, with questions about their level of
engagement in training activities, as well as education, interests,
hobbies, careers, accomplishments, and other characteristics that
may be informative about potential predictors of expertise (i.e.,
performance differences). The survey data are then analyzed
to identify a candidate set of potential predictors of expertise.
Finally, a study is conducted to estimate the relative contributions
of the factors to the prediction of individual differences in
expertise. Programmatic research can then proceed on this basis.
A major goal of the GECA is to identify activities that
consistently correlate to a practically and statistically significant
degree with measures of expertise. These activities may differ
across domains. More specifically, for some domains, the
activities may meet the ultimate criteria for deliberate practice,
whereas for other domains, they may include unstructured
activities that do not fit the definition of deliberate practice,
purposeful practice, or even naïve practice. Furthermore, within
a domain, there may be multiple “routes” to developing expertise.
That is, one performer may achieve a given level of expertise
by engaging in one set of activities, whereas another performer
may achieve the same level of expertise by engaging in a
different set of activities. As an illustration, Berliner (1994) found
that jazz musicians emphasized the importance of intentionally
unstructured “jam” sessions for developing improvisational skill,
and noted that “[s]trongly motivated students commonly learned
musical instruments without formal instruction by synthesizing
bits of knowledge from commercial method books, other young
performers, and their own experimentation” (Location 738).
In short, the search for activities using Friedlander and
Fine’s (2016) GECA holds great potential to shed light on
the role of different types of training in explaining expertise
across a wide range of domains, including not only domains
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Hambrick et al. The Deliberate Practice View
traditionally studied in expertise research (e.g., chess, sports, and
classical music) but also those that have received relatively little
attention in research.
CONCLUSION
For decades, the field of expertise has focused on environmental
factors as the major determinants of individual differences in
expertise, whereas genetically influenced factors are assumed
to play a relatively unimportant role, if any role at all
(Ericsson et al., 1993;Ericsson, 2007a). Environmental factors
certainly are important to consider in investigating the origins
of individual differences in expertise, but a comprehensive
scientific theory of expertise must take into account genetically
influenced factors as well, including basic abilities and capacities
(“talent”). At a more general level, we argue that it is
time—past time—for the nature vs. nurture debate to be
over in the field of expertise, as it has been in most areas
of psychological research for decades (Turkheimer, 2000).
Embracing the idea that expertise can be best understood as
a product of gene-environment interplay (nature and nurture)
will, as Plomin (2018) recently observed, move the field ahead
and integrate it with the life sciences. At a practical level,
findings from this research will provide a scientific foundation
for principles and procedures designed and implemented to
accelerate people’s acquisition of complex skills across a wide
range of domains and elevate the performance of individuals,
organizations, and societies.
REFLECTION
As we were editing the page proofs for this article, we received the
sad news of Anders Ericsson’s passing. It is difficult to imagine
the field of expertise without Anders—he was a pioneer. But his
ideas will live on and continue to inspire scholarship and debate
that will lead to greater understanding of the subject about which
he was so passionate. We hope that Anders found our work as
stimulating as we found his. He forced us to think critically about
our most basic assumptions concerning expertise, and to try to
put our best case for our perspective forward. We are in his
debt, and extend our sincere condolences to his family, friends,
students, and colleagues.
AUTHOR CONTRIBUTIONS
DH was the primary author and conceived the article. BM
and FO provided extensive comments and edits on multiple
drafts. All authors contributed to the article and approved the
submitted version.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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Hambrick et al. The Deliberate Practice View
APPENDIX
Ericsson & Pool (2016) introduce
disncon between deliberate
pracce and purposeful pracce
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Year
“In chess, Charness and his colleagues (Charness,
Krampe, & Mayr, 1996; Charness, Tuffiash,
Krampe, Reingold, & Vasyukova, 2005) have
found that the amount of solitary chess study
was the best predictor of performance at chess
tournaments, and when this type of deliberate
pracce was stascally controlled, there was no
reliable benefit from playing chess games.
-Ericsson (2014), p. 190
2019
“The accumulated amount of
‘solitary chess study’
(purposeful pracce) was
found to be the best predictor
of chess rang (beta = 0.45) for
mature chess players, whereas
the total amount of me
playing chess games (naïve
pracce) did not account for
significant addional variance
in performance
for this group (Charness,
Tuffiash, Krampe, Reingold, &
Vasyukova, 2005).”
- Ericsson (2020), p. 168
“More recently, Ericsson and
Pool (2016) addressed this
conceptual confusion and
proposed the term purposeful
pracce for individualized
pracce acvies which the
trainee engages in to improve
their performance but without
the benefit of a teacher with
extensive knowledge of
effecve methods for pracce.
This type of pracce is well
illustrated by the serious
pracce alone in chess
(Charness et al., 1996).”
- Ericsson & Harwell (2019),
p. 5
Conflicng characterizaon
within same arcle,
Ericsson & Harwell (2019)
In commentary on
Macnamara et
al.’s (2014) meta-
analysis, Ericsson
(2014) rejects
Charness et al.
(2005) studies for
violang a
criterion of DP
*The inial report of the data was
in a chapter by Charness, Krampe,
& Mayr (1996); the full report was
in Charness et al. (2005), Study 1
and Study 2
But then back to
purposeful pracce
in subsequent arcle
“The paper by Charness, Tufash,
Krampe, Reingold, and Vasyukova
…extends an earlier classic chapter by
Charness, Krampe, and Mayr (1996) and
examines retrospecve esmates by a
large sample of chess players about
their training during the development
of their skill and experse. This paper
reports the most compelling and
detailed evidence for how designed
training (deliberate pracce) is the
crucial factor in developing expert
chess performance.”
- Ericsson (2005), p. 237
“Charness et al. (2005) found
evidence for an independent
effect of engagement in
purposeful pracce for chess
skill, even aer controlling for
other types of pracce
acvies.”
- Moxley, Ericsson, &
Tuffiash (2019), p. 1163
Var yin g characterizaons of Charness and
colleagues’ chess studies* as evidence concerning
deliberate pracce vs. purposeful pracce
(boldface added on terms in quotaons)
2020
But for their meta-analysis,
Ericsson & Harwell (2019)
coded Charness et al. (2005),
which includes the data from
Charness et al. (1996), as
deliberate pracce instead of
purposeful pracce
FIGURE A1 | Varying characterizations of Charness et al. (2005) chess studies in Ericsson’s writings.
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