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Crowdsourcing Hypothesis Tests: Making Transparent How Design Choices
Shape Research Results
ArticleinPsychological Bulletin · October 2019
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CROWDSOURCING HYPOTHESIS TESTS 1
Running head: Crowdsourcing hypothesis tests
Landy, J. F., Jia, M., Ding, I. L., Viganola, D., Tierney, W., . . . Uhlmann, E. L. (in press),
“Crowdsourcing hypothesis tests: Making transparent how design choices shape research
results,” Psychological Bulletin.
Crowdsourcing hypothesis tests:
Making transparent how design choices shape research results
(In Press at Psychological Bulletin)
Justin F. Landy1, Miaolei (Liam) Jia2, Isabel L. Ding3, Domenico Viganola4, Warren Tierney5,
Anna Dreber6,7, Magnus Johannesson6, Thomas Pfeiffer8, Charles R. Ebersole9, Quentin F.
Gronau10, Alexander Ly10,11, Don van den Bergh10, Maarten Marsman10, Koen Derks12, Eric-Jan
Wagenmakers10, Andrew Proctor6, Daniel M. Bartels13, Christopher W. Bauman14, William J.
Brady15, Felix Cheung16, Andrei Cimpian15, Simone Dohle17, M. Brent Donnellan18, Adam
Hahn17, Michael P. Hall19, William Jiménez-Leal20, David J. Johnson21, Richard E. Lucas18,
Benoît Monin22, Andres Montealegre20,23, Elizabeth Mullen24, Jun Pang25, Jennifer Ray15, Diego
A. Reinero15, Jesse Reynolds22, Walter Sowden19,26, Daniel Storage27, Runkun Su3, Christina M.
Tworek28, Jay J. Van Bavel15, Daniel Walco29, Julian Wills15, Xiaobing Xu30, Kai Chi Yam3,
Xiaoyu Yang31, William A. Cunningham32, Martin Schweinsberg33, Molly Urwitz6, The
Crowdsourcing Hypothesis Tests Collaboration, Eric L. Uhlmann34
1Nova Southeastern University, 2Warwick Business School, University of Warwick, 3National
University of Singapore, 4George Mason University, 5Kemmy Business School, University of
Limerick, 6Stockholm School of Economics, 7University of Innsbruck, 8Massey University,
9University of Virginia, 10University of Amsterdam, 11Centrum Wiskunde & Informatica,
12Nyenrode Business University, 13University of Chicago, 14University of California, Irvine,
15New York University, 16University of Hong Kong, 17University of Cologne, 18Michigan State
University, 19University of Michigan, Ann Arbor, 20Universidad de los Andes, 21University of
Maryland at College Park, 22Stanford University, 23Cornell University, 24San José State
University, 25Renmin University of China, 26Tripler Army Medical Center, Hawaii, 27University
of Denver, 28HarrisX, 29New York Yankees, 30Hainan University, 31Tsinghua University,
32University of Toronto, 33ESMT Berlin, 34INSEAD
Author contributions: The 1st through 4th and last authors developed the research questions,
oversaw the project, and contributed equally. The 1st through 3rd authors oversaw the Main
Studies and Replication Studies, and the 4th, 6th, 7th, and 8th authors oversaw the Forecasting
Study. The 1st, 4th, 5th, 8th, and 9th authors conducted the primary analyses. The 10th through 15th
authors conducted the Bayesian analyses. The first and 16th authors conducted the multivariate
CROWDSOURCING HYPOTHESIS TESTS 2
meta-analysis. The first through ninth and last authors drafted the manuscript. The 17th through
47th authors developed the materials used in the Main Studies and Replication Studies. The 48th
author assisted in designing the Forecasting Study. The members of the Crowdsourcing
Hypothesis Tests Collaboration lent their expertise as evaluators of study quality and forecasters
(see Appendix for details). All authors revised and approved the final manuscript.
Acknowledgements. Data collection was funded by a research grant from INSEAD and a grant
from the Jan Wallander and Tom Hedelius Foundation (Svenska Handelsbankens
Forskningsstiftelser) to Anna Dreber. Anna Dreber is also funded by the Knut and Alice
Wallenberg Foundation (through a Wallenberg Academy Fellows grant) and the Austrian
Science Fund (FWF, SFB F63), and Anna Dreber, Magnus Johannesson and Domenico Viganola
are funded by the Jan Wallander and Tom Hedelius Foundation (Svenska Handelsbankens
Forskningsstiftelser) and the Swedish Foundation for Humanities and Social Sciences. Thomas
Pfeiffer is funded by Marsden Fund grants 16-UOA-190 and 17-MAU-133.
Contact Authors: email@example.com, firstname.lastname@example.org, email@example.com,
CROWDSOURCING HYPOTHESIS TESTS 3
To what extent are research results influenced by subjective decisions that scientists make as
they design studies? Fifteen research teams independently designed studies to answer five
original research questions related to moral judgments, negotiations, and implicit cognition.
Participants from two separate large samples (total N > 15,000) were then randomly assigned to
complete one version of each study. Effect sizes varied dramatically across different sets of
materials designed to test the same hypothesis: materials from different teams rendered
statistically significant effects in opposite directions for four out of five hypotheses, with the
narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective
on the results revealed overall support for two hypotheses, and a lack of support for three
hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill
of the research team in designing materials, while considerable variability was attributable to the
hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly
correlated with study results, both across and within hypotheses. Crowdsourced testing of
research hypotheses helps reveal the true consistency of empirical support for a scientific claim.
Keywords: Crowdsourcing, scientific transparency, stimulus sampling, forecasting, conceptual
replications, research robustness
Public Significance Statement: Research in the social sciences often has implications for public
policies as well as individual decisions—for good reason, the robustness of research findings is
therefore of widespread interest both inside and outside academia. Yet, even findings that
directly replicate – emerging again when the same methodology is repeated – may not always
prove conceptually robust to different methodological approaches. The present initiative suggests
that crowdsourcing study designs using many research teams can help reveal the conceptual
robustness of the effects, better informing the public about the state of the empirical evidence.
CROWDSOURCING HYPOTHESIS TESTS 4
Crowdsourcing hypothesis tests:
Making transparent how design choices shape research results
Scientific theories are meant to be generalizable. They organize findings, ideas, and
observations into systems of knowledge that can make predictions across situations and contexts.
Theories are more useful when they can explain a wider variety of phenomena. Understanding a
theory’s scope is critical to successfully applying it. In order to be generalizable, theories often
make use of abstract concepts or conceptual variables to organize their hypothesized
relationships. For instance, cognitive dissonance theory, one of the most influential theories in
social psychology, states that when individuals have inconsistent cognitions, they will experience
psychological distress or discomfort that motivates them to reduce the inconsistency (Festinger,
1957). This theory makes use of conceptual variables to describe its relationships of interest. In
particular, “cognitions” refer to any of several types of mental constructs, including attitudes,
beliefs, self-concepts, and knowledge that one has engaged in a certain behavior. Reducing
inconsistency can take many forms, such as altering one or both of the cognitions to become
consistent, or adding new cognitions that resolve the discrepancy. These conceptual variables
allow researchers to use the theory to make predictions about many different situations in which
people experience inconsistency.
Researchers must operationalize abstract and conceptual variables into concrete terms for
empirical testing. For example, to study cognitive dissonance, a researcher might identify two
cognitions that could reasonably be brought into conflict with one another (the independent
variable). Then, the psychologist could identify a way of resolving the conflict to provide to
participants (the dependent variable). Indeed, psychologists have studied cognitive dissonance by
measuring attitudes toward a boring task after inducing some participants to lie to the next
participant and say the task was exciting (Festinger & Carlsmith, 1957), by measuring
preferences toward appliances after obliging participants to choose between two attractive
options to receive as a gift (Brehm, 1956), or by assessing interest in a study group after
undergoing an uncomfortable initiation (Aronson & Mills, 1959). Each of these concrete
operationalizations widens the understood boundaries of the conceptual variables involved in an
effect and thus the generalizability of the effect itself (Schmidt, 2009; Stroebe & Strack, 2014).
Although generalizability is a critical goal of scientific research, the standard model of
conducting research creates many challenges for establishing robust generalizability of an effect
CROWDSOURCING HYPOTHESIS TESTS 5
across contexts. Researchers and/or labs often work in isolation or in small groups, generating
their own hypotheses, measures, and operationalizations. These operationalizations represent a
small subset of the possible, theoretically justifiable methods that they could have used to test
their hypotheses (Baribault et al., 2018; Judd, Westfall, & Kenny, 2012; Monin & Oppenheimer,
2014; Monin, Pizarro, & Beer, 2007; Wells & Windschitl, 1999; Westfall, Judd, & Kenny,
2015). In particular, scientists may use methods that are likely to confirm their preconceptions
(McGuire, 1973, 1983; Nickerson, 1998). For example, researchers who theorize that moral
judgments are intuitive tend to use simple and emotionally evocative scenarios, whereas
researchers who theorize that moral judgments are rooted in reasoning tend to use complex
stimuli that pit different values against each other and stimulate deliberation (Monin et al., 2007).
Such assumptions may guide which operationalizations are used to test hypotheses and theories,
and divergence across operationalizations may then affect which theory is empirically supported.
After one or a few operationalizations and stimulus sets are tested, researchers choose
which observations to report to the broader scientific community in academic journals. There is
substantial evidence that scientific publishing is biased in favor of positive or statistically
significant findings, leaving negative and null results underreported (Greenwald, 1975;
Ioannidis, 2005; Ioannidis & Trikalinos 2007; Pfeiffer, Bertram, & Ioannidis, 2011; Rosenthal,
1979; Schimmack, 2012; Simonsohn, Nelson, & Simmons, 2014). Null results are important for
understanding generalizability because they provide insights about where the boundaries of a
theory lie; nonetheless, the scientific community may be left largely unaware of them due to
biases in publishing (LeBel, McCarthy, Earp, Elson, & Vanpaemel, 2018; Zwaan, Etz, Lucas, &
After initial observations are reported, other researchers may conduct follow-up research.
These follow-ups have the potential to increase understanding of generalizability by inspiring
new operationalizations and instantiations of effects and theories. Still, scientific culture and
professional advancement often privilege novelty over increased certainty and incremental
refinement (Everett & Earp, 2015; Giner-Sorolla, 2012; Nosek, Spies, & Motyl, 2012), which
may disincentivize researchers from conducting tests of previously published ideas in favor of
pursuing new ideas and theories (Makel, Plucker, & Hegarty, 2012). Although scientific culture
has been changing with respect to valuations of replications, particularly in psychology, these
changes have been more focused on direct replications (testing the same idea with the same
CROWDSOURCING HYPOTHESIS TESTS 6
materials and methodology; Alogna et al., 2014; Ebersole et al., 2016; Klein et al., 2014; Open
Science Collaboration, 2015; Pashler & Harris, 2012; Simons, 2014) than on conceptual
replications (testing established ideas with a new approach; Crandall & Sherman, 2016; Finkel,
Eastwick, & Reis, 2015, 2017). Furthermore, failed conceptual replications are far more
susceptible to alternative explanations based on methodological differences than are direct
replications, and as a consequence may be left unpublished or dismissed by original researchers
and other scientists (Baribault et al., 2018; Doyen, Klein, Simons, & Cleeremans, 2014; Earp, in
press; Hendrick, 1990; Schmidt, 2009; Simons, 2014). Taken together, these forces within the
standard model of conducting psychological research may impede tests of generalizability of
scientific theories and phenomena. The standard model may thus stunt theory development by
limiting contributions to the literature to ones based on a relatively small subset of
operationalizations, and to unrealistically positive results.
The Current Research
To address these challenges, we introduce a crowdsourced approach to hypothesis
testing. In the crowdsourcing initiative reported here, up to 13 research teams (out of a total of 15
teams) independently created stimuli to address the same five research questions, while fully
blind to one another’s approaches, and to the original methods and the direction of the original
results. The original hypotheses, which were all unpublished at the time the project began, dealt
with topics including moral judgment, negotiations, and implicit cognition. Large samples of
research participants were then randomly assigned to different teams’ versions of the same study,
with a commitment to publish the results from all study designs as a fundamental component of
the project. The analyses were also pre-registered, which has been argued to reduce bias (Nosek,
Ebersole, DeHaven, & Mellor, 2018; Wagenmakers, Wetzels, Borsboom, van der Maas, &
Kievit, 2012; Wicherts, Veldkamp, Augusteijn, Bakker, Van Aert, & Van Assen, 2016), although
a causal effect remains to be empirically demonstrated. Comparisons of the estimated effect sizes
associated with the same hypothesis across the studies created by the different teams reveal the
extent to which the empirical results are contingent on the decisions scientists make as they
design their study. Aggregating results across teams via meta-analysis, taking into account both
average effects and variability across teams, provides a systematic assessment of the relative
strength of support for each hypothesis.
CROWDSOURCING HYPOTHESIS TESTS 7
There are a number of potential benefits to a crowdsourcing approach to hypothesis
testing. Crowdsourcing the operationalization of research ideas makes transparent the true
consistency of support for an empirical prediction, and provides a more stringent test of
robustness than employing a narrow set of stimuli (Monin & Oppenheimer, 2014), directly
replicating multiple independent and dependent variables that have been used previously
(Caruso, Shapira, & Landy, 2017), or even the innovative approach of radically randomizing
features of the same basic experimental design (e.g., symbols, colors, and presentation speeds in
a cognitive priming paradigm; Baribault et al., 2018). Rather than varying features of the same
basic design to address concerns about stimulus sampling (Baribault et al., 2018), we had
different researchers design distinct studies to test the same research questions, providing an
arguably wider-ranging test of the conceptual robustness of each original finding. The extent to
which divergent approaches produce different results is further revealed. Uniquely, the
conceptual replications are developed by independent research teams with no prior knowledge of
the original authors’ method or results to bias them (Silberzahn et al., 2018; Silberzahn &
Uhlmann, 2015), unlike in the usual practice of science, in which conceptual replications are
conducted after the dissemination of the original results. Materials designers also did not know
the direction of the original hypotheses and results, but were rather provided with a non-
directional version of each research question (see below). This was to prevent materials
designers from constructing materials aimed at confirming a directional hypothesis, while not
giving alternative directional hypotheses a chance (Monin et al., 2007). In other words, we
believe that we were more concerned with answering the research questions that drove the five
original, unpublished studies than we were with confirming their results.
Because all crowdsourced conceptual replications were pre-registered and reported, this
approach is also free of reporting bias, unlike traditional conceptual replications where null
effects may be attributed to departures from the original methodology and therefore left
unpublished. Moreover, because participants from the same large sample are randomly assigned
to different conceptual replications, discrepant results, including “failed” replications, cannot be
attributed to differences in the populations being sampled (McShane, Tackett, Böckenholt, &
Gelman, 2019; Tiokhin, Hackman, Munira, Jemsin & Hruschka, 2019; Van Bavel, Mende-
Siedlecki, Brady, & Reinero, 2016). Heterogeneity in results above-and-beyond what would be
expected based on sampling error can confidently be attributed to design choices.
CROWDSOURCING HYPOTHESIS TESTS 8
In the present initiative, we also recruited a second large sample and repeated our initial
studies with the same methodologies and materials. This effort is, to our knowledge, the first
time an entire crowdsourced set of studies has itself been directly replicated. Doing so allowed us
to simultaneously take into account both conceptual and direct replications when assessing the
strength of evidence for each finding. Altogether, we provide a new framework for determining
the generalizability and context-dependency of new findings, with the goal of identifying more
deeply robust phenomena, which we believe may hold utility for select research questions in the
future. In the Discussion, we elaborate at greater length on when crowdsourcing hypothesis tests
is likely to prove most (and least) useful.
We additionally examine whether scientists are able to predict a priori how design
choices impact research results. Prior work has demonstrated that researchers can anticipate
whether a published result will independently replicate based on the research report alone
(Camerer et al., 2016; Dreber et al., 2015), and predict the effects of performance interventions
starting only from a few benchmark effects and the materials for the additional treatments
(DellaVigna & Pope, 2018a, 2018b). Other forecasting studies with scientists have returned more
mixed results (Coffman & Niehaus, 2014; Dunaway, Edmonds, & Manley, 2013; Groh,
Krishnan, McKenzie, & Vishwanath, 2016; Sanders, Mitchell, & Chonaire, 2015). We therefore
conducted a forecasting survey asking an independent crowd of scientists to attempt to predict
the results of each study based solely on its sample size, methodology, and materials. Notably,
all prior work has examined forecasting accuracy across different hypotheses that vary in their
truth value and alignment with empirical reality. In contrast, we assessed whether scientists are
accurate in their beliefs about the outcomes of different experiments designed to test the same
research question. Scientists’ intuitions about the impact of researcher choices may or may not
map onto the actual downstream consequences.
Main Studies and Replication Studies
In two separate data collection efforts (the initial investigations [“Main Studies”] and
direct replications [“Replication Studies”]), we randomly assigned participants to different sets
of study materials designed independently by up to 13 teams of researchers to test the same five
research questions. There were 15 teams of materials designers in total, from which up to 13
teams designed materials for each research question (i.e., not all teams made materials to test all
CROWDSOURCING HYPOTHESIS TESTS 9
five original hypotheses). The five research questions were gathered by emailing colleagues
conducting research in the area of moral judgment and asking if they had initial evidence for an
effect that they would like to volunteer for crowdsourced testing by other research groups. In
three cases (Hypotheses 1, 3, and 4), project coordinators volunteered an effect from their
research program that fit these criteria, and in two cases, members of other teams volunteered an
effect (Hypotheses 2 and 5). In the present research, we examined the overall degree of support
for each hypothesis, and also quantified the heterogeneity across different sets of study materials.
To our knowledge, this instance is the first time a large-scale meta-scientific project has itself
been directly replicated in full with a new sample. All materials, data, and analysis scripts from
this project are publicly available at https://osf.io/9jzy4/.
Target hypotheses. We identified five directional hypotheses in the areas of moral
judgment, negotiation, and implicit cognition, each of which had been supported by one then-
unpublished study.1 Table 1 shows the directional hypotheses, as well as the nondirectional
forms in which they were presented to materials designers. Below we elaborate briefly on the
theoretical basis for each research question.
Hypothesis 1: Awareness of automatic prejudice. Influential dual-process theories of
intergroup attitudes propose that individuals have both explicit, consciously endorsed attitudes
towards negatively stereotyped groups, and also implicit ones that they may not endorse
(Dovidio, Kawakami, & Gaertner, 2002; Fazio, Jackson, Dunton, & Williams, 1995; Gawronski
& Bodenhausen, 2006; Greenwald & Banaji, 1995; Wilson, Lindsey, & Schooler, 2000). Rather
than in propositional logic, these implicit attitudes are based in simple associations (e.g., Black-
Criminal, Female-Weak), that are conditioned by the cultural environment (Gawronski &
Bodenhausen, 2006; Uhlmann, Poehlman, & Nosek, 2012). As a result, even consciously
egalitarian individuals often harbor prejudiced associations that may “leak out” and affect their
judgments and behaviors without them realizing it. Low correspondence between self-reported
and implicit measures of intergroup attitudes has been interpreted as indicating a lack of
introspective access into the latter (Banaji, 2001; Greenwald & Banaji, 1995). Nonetheless,
people could potentially be aware of their spontaneous affective reactions without endorsing
them. Indeed, Hahn and colleagues (Hahn, Judd, Hirsh, & Blair, 2014; Hahn & Gawronski,
1 The original study supporting Hypothesis 4 has since been published as a supplemental study in Landy, Walco,
and Bartels (2017).
CROWDSOURCING HYPOTHESIS TESTS 10
2019) provide evidence that people can accurately predict their performance on Implicit
Association Tests of associations with social groups (Greenwald, McGhee, & Schwartz, 1998).
Uhlmann and Cunningham (2000) constructed questionnaire items examining whether
individuals directly self-report negative gut feelings towards minorities. Representative items
include “Although I don't necessarily agree with them, I sometimes have prejudiced feelings
(like gut reactions or spontaneous thoughts) that I don't feel I can prevent” and “At times
stereotypical thoughts about minorities coming into my head without my necessarily intending
them to.” In the original research, approximately three-quarters of undergraduates agreed with
such statements, and overall endorsement was confirmed by mean responses statistically
significantly above the neutral scale midpoint of four (1= strongly disagree, 4= neutral, 7=
strongly agree). As the Uhlmann and Cunningham (2000) investigations were never published,
the present initiative crowdsourced the question of whether people self-report automatic
intergroup prejudices, assigning a dozen independent research teams to create their own
awareness measures. Specifically, we examined whether the majority of people, without further
prompting or consciousness-raising, agree on questionnaire measures that they harbor such
automatic biases towards stigmatized groups.
Hypothesis 2: Extreme offers reduce trust. Negotiators are routinely advised to make
extreme first offers to benefit from the anchoring effect (Tversky & Kahneman, 1974). When
sellers make extreme first offers, final prices tend to be high; in contrast, when buyers make
extreme first offers, final prices tend to be low (Ames & Mason, 2015; Galinsky, Leonardelli,
Okhuysen, & Mussweiler, 2005; Galinsky & Mussweiler, 2001). Evidence suggests this effect is
robust across cultures, issues, and power positions (Gunia, Swaab, Sivanathan, & Galinsky,
2013). Yet, more recent research has examined the conditions under which this advice might not
be accurate (Loschelder, Swaab, Trötschel, & Galinsky, 2014; Loschelder, Trötschel, Swaab,
Friese, & Galinsky, 2016; Maaravi & Levy, 2017). The present Hypothesis 2 explores one
mechanism for why extreme first offers might backfire in negotiations with multiple issues.
Specifically, extreme first offers may interfere with value creation processes such as trust
building and information exchange. Building on previous research that showed that extreme first
offers can cause offense and even impasses (Schweinsberg, Ku, Wang, & Pillutla, 2012),
Schweinsberg (2013) examined the specific hypothesis that extreme first offers lower trust in the
counterpart. Ultimately, this line of research may show that extreme first offers can help
CROWDSOURCING HYPOTHESIS TESTS 11
negotiators claim a larger percentage of the bargaining zone for themselves, but that extreme first
offers also shrink the overall size of the bargaining zone by reducing information exchange and
trust. Thus, extreme first offers might help negotiators claim a larger percentage of a smaller
bargaining zone, making them ultimately worse off. Negotiators might be blind to this extreme
first offer disadvantage because their salient comparison is between value they claimed versus
value claimed by their counterpart, and not the more relevant but counterfactual comparison
between value they claimed from an extreme offer versus value they could have claimed from a
more moderate first offer. The present research focuses on just one part of this argument,
providing crowdsourced tests of the prediction that “negotiators who make extreme first offers
are trusted less, relative to negotiators who make moderate first offers.”
Hypothesis 3: Moral praise for needless work. It is easy to find anecdotal examples in
which individuals received moral praise for continuing to work despite coming into sudden
wealth and no longer needing to earn a salary (Belsie, 2011). In scenario studies based on such
real life cases, Americans positively evaluate the moral character of individuals with working
class occupations (e.g., potato peeler in a restaurant kitchen) who continue their employment
after winning the lottery (Poehlman, 2007; Uhlmann, Poehlman, & Bargh, 2009). A number of
sources for such moral intuitions are plausible, among these a tendency to value work
contributions that parallels general disapproval of shirkers and non-contributors (Jordan,
Hoffman, Bloom, & Rand, 2016), use of work behavior as a signal of underlying traits
(Uhlmann, Pizarro, & Diermeier, 2015), the influence of the Protestant work ethic in some
cultures (Uhlmann & Sanchez-Burks, 2014), and post-materialist value systems in which work is
pursued for meaning and fulfillment rather than as an economic necessity (Inglehart, 1997;
Inglehart & Welzel, 2005). A separate project to this one examines the extent to which these and
other work morality effects directly replicate across different national cultures (Tierney et al.,
2019a). Of interest to the present initiative is how conceptually robust the findings are to
alternative study designs. We therefore crowdsourced the research question of whether working
in the absence of material need elicits moral praise, limiting our samples to U.S.-based
participants, the group originally theorized to exhibit these effects.
Hypothesis 4: Proximal authorities drive legitimacy of performance enhancers. People in
the United States express widespread normative opposition to the use of Performance-Enhancing
Drugs (PEDs), especially among competitive athletes, but it is not clear what underpins these
CROWDSOURCING HYPOTHESIS TESTS 12
judgments. While most studies of opposition to PEDs have examined perceptions of fairness
(e.g., Dodge, Williams, Marzell, & Turrisi, 2012; Fitz, Nadler, Manogaran, Chong, & Reiner,
2014; Scheske & Schnall, 2012), some research also suggests that the sheer fact that PEDs are
prohibited also contributes to opposition toward them (Sattler, Forlini, Racine, & Sauer, 2013).
This distinction between fairness concerns and explicit rules roughly parallels the insight from
Social Domain Theory (Turiel, 1983; 2002) that acts can be “wrong” in at least two qualitatively
different ways: moral offenses violate universal moral standards like fairness, whereas
“conventional” offenses violate consensually accepted norms or the dictates of legitimate
authorities. Landy, Walco, and Bartels (2017) investigated whether opposition to PED use
exhibits properties of conventional offenses by manipulating whether or not an athlete’s use of
PEDs “violates the law and the rules of his [competition] circuit” (Study 2 of the original report),
and found that this manipulation significantly affected people’s judgments of how wrong it was
for the athlete to use PEDs. A follow-up study (Supplemental Study 1 of the original report)
found that PED use was considered more wrong when it violated a dictate of a legitimate
proximal authority (the competition circuit) than when it violated the law. An additional study
replicated this finding (Study 12 of the original report), but a further study did not (Study 13 of
the original report), so it is unclear whether proximal authority or legal authority contributes
more to opposition to PED use. Since all of these studies were unpublished at the beginning of
this project, we applied our crowdsourcing methodology to obtain a more definitive answer to
Hypothesis 5: The tendency to make deontological judgments is positively correlated with
happiness. In order to bridge the normative-descriptive divide between the fields of
philosophical ethics (how should people morally behave) and moral psychology (how and why
do people morally behave) cognitive science must map out how variation in moral cognitions are
systematically related to variances in outcomes related to human flourishing. The goal of this
original research was to contribute to this endeavor by examining how the tendency to make
utilitarian versus deontological moral judgments (Bentham 1970/1823; Kahane, 2015; Kant,
1993/1785; Mill, 1861) relates to personal happiness and well-being (Kahneman, Diener, &
Schwarz, 1999; Ryff, 1989; Waterman, 1993). The idea that happiness and morality are tightly
intertwined has a long history in philosophy (see, e.g., Annas, 1993; Aristotle, 340 BCE/2002;
Foot, 2001; Kraut, 1979), and recent empirical work suggests that people consider moral
CROWDSOURCING HYPOTHESIS TESTS 13
goodness to be an element of what “happiness” consists of (Phillips, Freitas, Mott, Gruber, &
Knobe, 2017; Phillips, Nyholm, & Liao, 2014). However, prior work has not examined the
relationship (if any) between specific moral orientations and happiness.
Hypothesis 5 posits that people who are more inclined to base their moral judgments on
the violation of rules, duties, and obligations (deontological judgments) versus material
outcomes (utilitarian judgments) are also more likely to experience happiness in their lives. This
prediction is based on philosophical and scientific evidence that has demonstrated shared
psychological and neurological mechanisms between these dimensions (e.g., Everett, Pizarro, &
Crockett, 2016; Greene, 2013; Lieberman, 2013; Phillips et al., 2017; Singer, 2005). To test this
hypothesis, Sowden and Hall (2015) asked participants to judge several morally questionable
behaviors that pitted utilitarian and deontological considerations against one another (Greene et
al. 2001) and compared an index of those judgments to how they responded to measures of
subjective well-being (Diener et al., 1985; Watson et al., 1988) and eudaimonic happiness
(Waterman et al., 2010). The crowdsourced project posed the research question to independent
researchers, who separately designed studies relating moral judgments to individual happiness.
[INSERT TABLE 1 ABOUT HERE]
Materials. A subset of the project coordinators (Landy, Jia, Ding, Uhlmann) recruited 15
teams of researchers through their professional networks to independently design materials to
test each hypothesis. Of these 15 teams, four included the researchers who developed the original
materials for at least one of the five hypotheses. Teams ranged in size from one researcher to
five, and members ranged in experience from graduate students to full professors. We opted not
to standardize team size because research teams vary greatly in size in the natural practice of
science (Wuchty, Jones, & Uzzi, 2007a, 2007b). All studies were required to be designed to be
administered in an online survey. Note that recruiting through our professional networks would,
if anything, be expected to bias our results towards homogeneity and consistency between
materials designers. Likewise, the restriction to using only brief, online questionnaires rather
than behavioral measures, video stimuli, or elaborate laboratory experiments with a cover story
and research confederates, also artificially constrains variability in study designs. Yet, as we
detail below, we still observed remarkable heterogeneity in results.
CROWDSOURCING HYPOTHESIS TESTS 14
To avoid biasing their designs, materials designers were provided with the non-
directional versions of the five hypotheses presented in Table 1, and developed materials to test
each hypothesis independently of the other teams. The team of Xu and Yang designed materials
only for Hypotheses 1, 2, and 5, and the team of Cimpian, Tworek, and Storage designed
materials only for Hypotheses 3 and 4. We also included the original materials from the
unpublished studies that initially supported each hypothesis and conducted direct replications
with them; the teams of Uhlmann, Schweinsberg, and Uhlmann and Cunningham only
contributed these original materials. The original materials for Hypothesis 5 were developed by
the team of Sowden and Hall, but were much longer than any other materials set, so this team
also developed a shorter set of materials for Hypothesis 5 and data were collected using both
versions. In all, 64 sets of materials, including the five sets of original materials, were created
through this crowdsourced process. The materials and analyses for both studies were pre-
registered at https://osf.io/9jzy4/ (see also Supplement 1, as well as Supplement 2 for deviations
from the pre-registered analyses).
Participants. In total, 8,080 participants located in the United States began the Main
Studies on Amazon Mechanical Turk (MTurk; Chandler, Mueller, & Paolacci, 2014, Chandler,
Paolacci, & Mueller, 2013); of these, 7,500 completed the entire study. In accordance with our
pre-registered stopping rule (see https://osf.io/avnuc/), we ceased data collection after N = 7,500
participants finished the survey. In the Replication Studies, 7,500 English-speaking adult
participants located in the United States were recruited via PureProfile, a survey firm – we
employed this different sampling method for the Replication because, in the Main Studies, we
had already essentially exhausted the number of Mechanical Turk participants that a typical lab
samples (see Stewart et al., 2015). In both data collection efforts, responses from participants
who completed their assigned materials for one or more hypotheses but did not complete the all
assigned materials in their entirety were retained, resulting in slightly different sample sizes
across the five hypotheses (Main Studies: Hypothesis 1 N = 7,175; Hypothesis 2 N = 7,160;
Hypothesis 3 N = 7,146; Hypothesis 4 N = 7,158; Hypothesis 5 N = 7,758; Replication Studies:
Hypothesis 1 N = 7,586; Hypothesis 2 N = 7,631; Hypothesis 3 N = 7,568; Hypothesis 4 N =
7,576; Hypothesis 5 N = 8,231). On a per-cell basis, there were approximately 300 participants
for Hypotheses 1–4, and 600 participants for Hypothesis 5 (which was tested using a Pearson
correlation, rather than a comparison between experimental groups).
CROWDSOURCING HYPOTHESIS TESTS 15
Procedure. In the Main Studies, participants were randomly assigned to one set of
materials for each of the five hypotheses, and, for designs with multiple conditions, one
condition per hypothesis. The order in which the five sets of materials were presented was
randomized for each participant. After responding to all five sets of materials, participants
completed a demographics questionnaire including questions about their age, gender, and other
characteristics. Additionally, a separate subsample of participants was randomly assigned to only
complete the full original materials for Hypothesis 5, due to their length. The materials designed
by the team of Jiménez-Leal and Montealegre to test Hypothesis 4 were run separately
approximately two months after the rest of Main Studies were run, because we discovered that,
due to a coding error, one of the two conditions from these materials was not presented to
participants in the original run (new data were therefore collected for both conditions of this
design). The procedure for the Replication Studies was essentially identical to that of the Main
Studies; the only modifications were fixing the aforementioned condition missing from
Hypothesis 4, and pre-registering some exploratory analyses conducted on the data from the
Main Studies (see Supplement 2), this time as confirmatory tests (see https://osf.io/8s69w/).
The online Forecasting Study was open to any scientist, and had two purposes. First, it
tested the extent to which researchers (N = 141) were able to predict the results of the Main
Studies and Replication Studies, in terms of the standardized effect size that would be obtained
from each set of materials, and also with regard to statistical significance (the likelihood that a p-
value below .05 would be found). Second, it determined how independent reviewers evaluate
each set of materials based on whether it provides an adequate test of the original hypothesis.
Variability across different study versions is far more meaningful if they provide valid tests of
the original research idea. We placed half of the forecasters at random into a monetarily
incentivized version of the survey; potential payoff ranged between $0 and $60, meaning
financial incentives were present in the treatment condition but not strong. Further
methodological details for the forecasting survey can be found in Supplements 3 and 5, and the
pre-registration can be found at https://osf.io/9jzy4/.
CROWDSOURCING HYPOTHESIS TESTS 16
Main Studies and Replication Studies
Given our key theoretical question regarding heterogeneity in estimates, as well as large
sample sizes that might render even small and theoretically uninteresting differences statistically
significant, our primary focus is on dispersion in effect sizes across different study designs. Yet,
since the p < .05 threshold is widely used as the lower bound criterion for concluding the
presence of an effect, we likewise examined patterns of statistical significance, both at the level
of individual designs and aggregated across them. This reliance on both effect sizes and
statistical significance levels to quantify the project results was pre-registered in advance.
Because of the potential issues associated with relying on statistical significance to draw
conclusions, we report the results of null hypothesis significance tests in Supplement 9, and
focus here on the analyses of effect sizes.
Meta-analytic statistics. To examine the support for each hypothesis, as well as the
variation across study designs for each of them, we computed effect size estimates for the results
from each of the 64 sets of materials. The diversity in effect size estimates from different study
designs created to test the same theoretical ideas constitute the primary output of this project. For
Hypotheses 1-4, the effect sizes were independent-groups Cohen’s ds, and for Hypothesis 5, they
were Pearson rs. Effect size estimates and sampling variances were calculated via bootstrapping,
using the bootES package for R (Kirby & Gerlanc, 2013)2, then combined in random-effects
meta-analyses using the metafor package (Viechtbauer, 2010), to obtain an overall estimate for
the size of each hypothesized effect.3 This model treats each observed effect size yi as a function
2 Materials designed by the team of Donnellan, Lucas, Cheung, and Johnson for Hypotheses 2, 3, and 4 employed
within-subjects designs, whereas the other materials for these hypotheses employed one-sample or between-subjects
designs. To ensure that all effect sizes were comparable in the meta-analyses, the repeated-measures ds for the
within-subjects designs were converted to independent-groups ds (see Morris & DeShon, 2002). bootES does not
have a feature to convert between effect size metrics, so custom bootstrapping code was used (see
https://osf.io/avnuc/). This custom code returns the same effect size estimates and variance terms for the repeated-
measures ds as bootES, and converts the repeated-measures ds to independent-groups ds according to Equation 11 in
Morris and DeShon (2002).
3 Fixed-effects models showed similar estimated mean effect sizes. In the Main Study, the point estimate was not
statistically significant for Hypothesis 1, p = .093, and was statistically significant for Hypothesis 4, p < .001. In the
Replication, the estimated effect sizes were again similar when fixed-effects models were used, but the point
estimates for Hypotheses 1 and 4 were statistically significant, p < .001. Yet, fixed-effects models are not generally
recommended when meta-analyzing studies with different methods (Borenstein, Hedges, Higgins, & Rothstein,
2010; Hunter & Schmidt, 2000), so we focus on the random-effects models.
CROWDSOURCING HYPOTHESIS TESTS 17
of the average true effect size µ, between-study variability, ui ∼ N(0, τ2), and sampling error, ei ∼
N(0, vi) (see Viechtbauer, 2010)4:
yi = µ + ui + ei
The heterogeneity among effect sizes (τ2) was estimated using Restricted Maximum
Likelihood Estimation. Positive effect sizes indicate results consistent with the original,
unpublished findings, whereas negative effect sizes indicate results in the opposite direction.
Figures 1a–1e present forest plots of the observed effect sizes in these analyses. For ease of
comparison across the five figures, the Pearson r effect sizes for Hypothesis 5 have been
converted to Cohen’s ds (Rosenthal & DiMatteo, 2001). The top panel of each figure presents
observed effect sizes and the estimated mean effect size from the Main Studies, and the middle
panel presents observed effect sizes and the estimated mean effect size from the Replication
Studies. Beneath these panels, the estimated mean effect size for each hypothesis, computed by
combining all individual effect sizes in the Main Studies and Replication Studies (k = 26 for
Hypotheses 1, 2, 3, and 5; k = 24 for Hypothesis 4) is presented. The bottom panel presents effect
sizes computed by meta-analytically combining the Main Studies’ and Replication Studies’
effect sizes for each set of materials (i.e., this panel presents the results of 12 or 13 meta-
analyses, each with k = 2 studies).5
[INSERT FIGURES 1A THROUGH 1E ABOUT HERE]
In the Main Studies, these analyses showed a statistically significant aggregated effect in
the expected direction for Hypotheses 2, 3, and 5 (estimated mean effect sizes: d = 1.04, 95% CI
[0.61, 1.47], p < .001; d = 0.33, 95% CI [0.17, 0.50], p < .001; r = .06, 95% CI [0.01, 0.11], p =
.010), and no statistically significant aggregated effect as expected under Hypotheses 1 and 4 (d
= 0.07, 95% CI [-0.22, 0.37], p = .623; d = 0.07, 95% CI [-0.05, 0.20], p = .269). Note that in the
4This analytic approach is not ideal, because it ignores the multivariate nature of the data: each hypothesis can be
thought of as a separate outcome variable. It also ignores the multilevel nature of the data (designs are nested within
hypotheses), and individual-level correlations across designs resulting from the fact that each participant completed
up to five different study designs. We therefore also ran a one-stage multivariate meta-analysis on our individual
participant data to model these aspects of the data. The results are very similar to the reported univariate meta-
analyses, and this approach has its own disadvantages, particularly that analysis of heterogeneity jointly across
outcomes jointly is complicated by the non-nested participant design (see Supplement 8). Therefore, we focus here
on the more familiar analytic approach.
5 When meta-analytically combining the Main Studies’ and Replication Studies’ effect sizes for each individual set
of materials, we employed fixed-effects models, unlike in the rest of our meta-analytic models. This is because the
two effect sizes being combined come from studies with identical materials and methods, so they should, in
principle, be measuring the same true population effect size.
CROWDSOURCING HYPOTHESIS TESTS 18
case of Hypothesis 5, the aggregated estimate was very small, and the threshold for statistical
significance may only have been crossed due to the large sample and the resultant high power to
detect even trivially small effects. In the Replication Studies, the patterns of results were similar,
though the estimated mean effect sizes tended to be somewhat smaller, overall. Hypotheses 2 and
3 (d = 0.60, 95% CI [0.32, 0.88], p < .001; d = 0.24, 95% CI [0.11, 0.38], p < .001) were
associated with a statistically significant effect in the expected direction. Hypothesis 5 did not
receive statistically significant overall support in the Replication Studies (r = .03, 95% CI [-.04,
.09], p = .417), though the estimated mean effect size was not meaningfully different than in the
Main Studies. Consistent with the Main Studies, Hypotheses 1 and 4 were again not supported
(d = -0.07, 95% CI [-0.33, 0.19], p = .588; d = 0.03, 95% CI [-0.05, 0.19], p = .465). Overall,
then, the meta-analytic results were largely consistent across the Main Studies and the
Replication Studies, reflecting overall support for Hypotheses 2 and 3, and an overall lack of
support for Hypotheses 1, 4, and 5. Similar results were found when relying on null hypothesis
significance testing (see Supplement 9).
Just as importantly, inspection of the forest plots suggests substantial variation among
effect sizes, even within the same hypothesis. We assessed this more formally by examining the
Q, I2, and τ2 statistics in each meta-analysis (Huedo-Medina, Sánchez-Meca, Marín-Martínez, &
Botella, 2006). The Q statistic is a test for heterogeneity - a significant Q statistic means that
heterogeneity in true effects can be expected. Because all participants in each study were drawn
from the same large online sample and randomly assigned to conditions, it is unlikely that
heterogeneity can be attributed to hidden moderators (e.g., different populations being sampled,
different study environments, etc., see Van Bavel et al., 2016), and thus is likely due to
differences in the materials. The I2 statistic quantifies the percentage of variance among effect
sizes attributable to heterogeneity, rather than sampling variance. By convention, I2 values of
25%, 50%, and 75% indicate low, moderate, and high levels of unexplained heterogeneity,
respectively (Higgins, Thompson, Deeks, & Altman, 2003). Yet, Q and I2 are also sensitive to
sample size; large samples tend to produce large and significant Q statistics and large I2 values.
Therefore, we also report the τ2 statistic as an absolute measure of the amount of heterogeneity in
our data. The τ2 statistic is an estimate of the variance of true effect sizes (Borenstein, Hedges,
Higgins, & Rothstein, 2009). All five hypotheses showed statistically significant and high levels
of heterogeneity in the Main Study and the Replication (see Table 2). In the Main Study, only
CROWDSOURCING HYPOTHESIS TESTS 19
about 1%, 2%, 6%, 12%, and 24% of the variance across the effect sizes for Hypotheses 1, 2, 3,
4, and 5, respectively, can be attributed to chance variation. Similarly, in the Replication, we
would only expect to observe about 1%, 3%, 9%, 22%, and 14% of the variance across the effect
sizes for Hypotheses 1, 2, 3, 4, and 5, respectively, by chance. The vast majority of observed
variance across effect sizes in both studies is unexplained heterogeneity. Moreover, the τ2
statistics are rather large, relative to the estimated mean effect sizes, suggesting that these large I2
values are not simply due to our large effect sizes resulting in low sampling variance - there are
meaningful levels of absolute heterogeneity in our data. One can also see this pattern simply by
visually inspecting the forest plots (Figures 1a-1e), which show considerable dispersion among
[INSERT TABLE 2 ABOUT HERE]
Explaining heterogeneity in effect sizes. We therefore sought to explain this observed
heterogeneity. First, we computed intraclass correlation coefficients (ICCs) predicting observed
effect sizes from the hypothesis they tested, and the team that designed the materials (see Klein
et al., 2014, for a similar analysis). In order to compare across all observed effect sizes, the
Pearson rs from Hypothesis 5 were converted into Cohen’s ds (Rosenthal & DiMatteo, 2001), as
above. In the Main Studies, the hypothesis being tested was moderately predictive of observed
effect sizes, ICC = .40, 95% CI [.15, .86], whereas team did not explain statistically significant
variance, ICC = -.13, 95% CI [-.23, .09]. The negative ICC for team indicates that between-team
variance is lower than within-team variance. This means that which team designed a set of
materials had no predictive relationship with the observed effect size (see Bartko, 1976). In other
words, some teams were not “better” than others at designing study materials that produced large
effect sizes across hypotheses. We followed up this analysis with a random-effects meta-
regression, predicting effect sizes from hypothesis and team, with the median hypothesis
(Hypothesis 5, in the Main Study) and the median team (Sowden & Hall) as the reference levels.
Hypothesis 2 produced statistically significantly larger effect sizes than the median hypothesis, β
= 0.85, 95% CI [0.47, 1.23], p < .001, but, consistent with the analysis above, no team produced
statistically significantly larger or smaller effect sizes than the median team, ps > .086.
Moreover, after accounting for both hypothesis and team, there was still substantial and
statistically significant residual heterogeneity across effect sizes, Q(44)= 1291.64, p < .001, I2=
97.39%, 95% CI [96.22, 98.40], τ2 = 0.24, 95% CI [0.16, 0.38]. In the present research, the
CROWDSOURCING HYPOTHESIS TESTS 20
subjective choices that researchers make in stimulus design have a substantial impact on
observed effect sizes, but if a research team produces a large effect size for one research
question, it does not necessarily mean that they will produce a large effect size for another
question. This pattern fails to support the hypothesis that some researchers have a “flair” for
obtaining large and statistically significant results (see, e.g., Baumeister, 2016). Still, more
research is needed on this point, since other research topics (e.g., stereotype threat, motivated
reasoning), or more finely parsed subtopics, may yet yield evidence for expertise effects in
conducting conceptual replications.6
As might be expected, independent ratings of the quality of each study design (assessed
in the Forecasting Study) were positively correlated with the obtained results. Higher quality sets
of materials yielded larger observed effect sizes in the direction predicted by each original
hypothesis (Cohen’s ds), r(62) = .31, p = .012. Thus, it is possible that the inclusion of low-
quality materials biases our analyses against finding support for hypotheses that are in fact true,
when properly tested. We therefore repeated all of the meta-analytic analyses above, excluding
18 sets of materials that were rated as below 5 on a scale of 0 (not at all informative) to 10
(extremely informative) by independent raters in the Forecasting Study. As described in greater
detail in Supplement 6, the results were substantively quite similar for all five hypotheses.
It is also possible that rather than artificially reducing the degree of observed support for
a given hypothesis, lower quality materials introduce psychometric artifacts such as poor
reliability and validity which bias effects toward zero. We therefore further examined whether
quality ratings predict larger effect size estimates in absolute terms, in other words larger
estimates either consistent or inconsistent with the original hypothesis. Independent ratings of the
quality of each study design were directionally positively correlated with the absolute value of
the effect size estimates, but this relationship was not statistically significant, r(62) = .20, p =
.12. Overall, the results suggest that the observed variability in effect sizes was not driven by a
subset of lower quality study designs.
Aggregating results of the Main Studies and Replication Studies. Leveraging the
combined samples of the Main Studies and Replication Studies allowed for more precise effect
6 We also re-ran these analyses, restricting the data to Hypotheses 3, 4, and 5, which are clearly within the same
general area of research, moral psychology, to see if we could find support for the flair hypothesis within a particular
area of research. Once again, however, we found no evidence that observed effect sizes are predicted by the identity
of the researchers that designed the materials (see Supplement 9 for details).
CROWDSOURCING HYPOTHESIS TESTS 21
size estimates from each study version, as well as higher-powered estimates of the overall degree
of support for each of the five original hypotheses. Aggregating all of the effect sizes across the
two studies in random-effects meta-analyses (k = 26 for Hypotheses 1, 2, 3, and 5; k = 24 for
Hypothesis 4) produced similar results to the separate meta-analyses above. Hypotheses 2 and 3
were supported (d = 0.82, 95% CI [0.55, 1.08], p < .001; d = 0.29, 95% CI [0.18, 0.39], p <
.001). Hypothesis 5 was also associated with a statistically significant estimate in the expected
direction, though, as above, the effect was negligible in size (r = .04, 95% CI [.01, .08], p =
.026), leading to the conclusion that H5 was not empirically supported by the crowdsourced
initiative. Later we report a Bayesian analysis casting further doubt on Hypothesis 5. Even under
the null hypothesis significance testing framework, Hypotheses 1 and 4 were not supported (d =
0.00, 95% CI [-0.19, 0.19], p = .997, and d = 0.05, 95% CI [-0.02, 0.13], p = .179). We repeated
these analyses selecting only study versions rated as 5 or above in informativeness by the
independent raters, (see Supplement 6), and nesting study (Main Studies versus Replication
Studies) within each hypothesis (see Supplement 9). Both of these additional analyses produced
qualitatively similar results to the results above.
Comparing the results of the Main Studies and Replication Studies. As there is no
single approach to determining whether an effect directly replicated or not (Brandt et al., 2014;
Open Science Collaboration, 2015), we pre-registered a number of criteria for whether the results
of the Main Studies held up in the Replication Studies. These included correlating the Main
Studies’ and Replication Studies’ effect sizes, comparing the statistical significance levels and
direction of effects, and testing for statistically significant differences between the effect sizes
from the Main Studies and the corresponding effect sizes from the Replication Studies. We
further examined whether the effect was statistically significant after meta-analyzing across both
the Main Studies and Replication Studies (see Figures 1a-1e), and we report a Bayesian analysis
of differences in the Main Study and Replication results in Supplement 7.
Each of these criteria is an imperfect and incomplete measure of replication. For instance,
a near perfect correlation in effect sizes could emerge even if replication effect sizes were
dramatically smaller, so long as the rank ordering of effects remained consistent. Given such a
pattern, it would be unreasonable to conclude the effects were robust and replicable. When it
comes to comparing whether the replication effect is statistically significantly different from the
original effect or not, this method is low in informational value when an original study has a
CROWDSOURCING HYPOTHESIS TESTS 22
statistically significant p-value close to .05 with a lower bound of the confidence interval close to
zero. With this p-value, it is highly unlikely to find a statistically significant difference from the
original result unless the replication point estimate is in the opposite direction of the original
With these caveats in mind, we turn to comparing the results from the Main Studies and
Replication Studies. In 51 out of 64 cases (80%), the Replication Studies’ effect was
directionally consistent with the effect from the Main Studies’. In 36 of those 51 cases (71%),
when new participants were run using the same study design, statistically significant results were
again statistically significant in the same direction, and non-significant effects were again non-
significant. Further, 13 of 44 (30%) statistically significant findings from the Main Studies were
not statistically significant in the Replication Studies. At the same time, 6 of 20 (30%) non-
significant findings from the Main Studies were statistically significant in the Replication
We next examined whether effect sizes were significantly different in size between the
two studies. We conducted z-tests comparing each team-by-hypothesis combination across the
two studies (e.g., Team 5’s materials for Hypothesis 1 from the Main Studies, versus Team 5’s
materials for Hypothesis 1 from the Replication Studies). Replication Studies’ effect sizes were
statistically significantly smaller than the corresponding effect in the Main Studies, according to
z-tests, in 21 out of 64 cases, and statistically significantly larger in just one case, with no
significant difference in 42 out of 64 cases. This pattern agrees with the qualitative observation
above that effect sizes tended to be somewhat smaller in the Replication Studies than in the Main
Studies. This was quite unexpected – if anything, we anticipated that Mechanical Turk, as the
less expensive, more expedient data source, might potentially yield smaller effect sizes. We can
only speculate that the general decline effect across the two samples resulted from the slightly
different populations of online respondents that were sampled, but the precise difference between
the two samples that drove this result is unclear.
When directly replicated, a substantial minority of individual effect sizes reversed
direction, changed significance levels across the p < .05 threshold, or were statistically
significantly different from the initial result. At the same time, correlating the 64 effect sizes
obtained in the Main Studies with the 64 effect sizes from the Replication Studies revealed very
high correspondence between them in the aggregate, r(62) = .92, 95% CI [.88, .95], p < .001 (see
CROWDSOURCING HYPOTHESIS TESTS 23
Figure 2). Moreover, descriptively, the major overall findings from our Main Studies emerged in
the Replication Studies as well. Effect sizes were again radically dispersed, with statistically
significant effects in opposing directions obtained from different sets of materials designed to
test three of the five research questions. Meta-analyzing across study versions, Hypotheses 2 and
3 were again supported, and Hypotheses 1 and 4 were not. The directional and statistically
significant, but very small estimate for Hypothesis 5 in the Main Studies was not statistically
significant in the Replication Studies, yet also not meaningfully different in size (Gelman &
Stern, 2006). Variability in effect sizes was again far more attributable to whether the hypothesis
itself enjoyed overall support than to the skill of particular research teams at designing studies
that returned large effects (see Supplement 9).
[INSERT FIGURE 2 ABOUT HERE]
Publication bias analyses. We present funnel plots and the results of Egger’s test
(Egger, Smith, Schneider, & Minder, 1997) for all of our meta-analytic results in Supplement 9.
Because all of the study designs are reported in this article, there is, by definition, no publication
bias in the results we have reported. Yet, we did find evidence of funnel plot asymmetries for
Hypotheses 1, 2, and 5. As we discuss in greater detail in Supplement 9, these must reflect
“sample size effects” that are idiosyncratic to the designs tested in this research. This result
highlights one further advantage of crowdsourcing in comparison to the traditional practice of
science: In a traditional meta-analysis of multiple studies conducted at different times, one
cannot be certain whether funnel plot asymmetries reflect publication bias or some other sample
size effect (see, e.g., Deeks, Macaskill, & Irwig, 2005), whereas in a crowdsourced project like
this one, there is, by the very nature of the design, no publication bias.
Bayesian perspective on the results. Supplement 7 provides an extended report of
Bayesian analyses of the overall project results (the pre-registered analysis plan is available at
https://osf.io/9jzy4/). To summarize briefly, the Bayesian analyses find compelling evidence in
favor of Hypotheses 2 and 3, moderate evidence against Hypothesis 1 and 4, and strong evidence
against Hypothesis 5. Overall, two of five original hypotheses were confirmed aggregating
across the different study designs. This pattern is generally consistent with the frequentist
analyses reported above, with the exception that the frequentist approach suggests a very small
but statistically significant (p < .05) effect in the direction predicted by Hypothesis 5 after
aggregating across the different study designs, whereas the Bayesian analyses find strong
CROWDSOURCING HYPOTHESIS TESTS 24
evidence against this prediction. The project coordinators, original authors who initially
proposed Hypothesis 5, as well as further authors on this article concur with the Bayesian
analyses that the effect is not empirically supported by the crowdsourcing hypotheses tests
project, due to the small estimate of the effect, and heterogeneity across designs. Regarding the
main meta-scientific focus of this initiative, namely variability in results due to researcher
choices, for all five hypotheses strong evidence of heterogeneity across different study designs
emerged in the Bayesian analyses.
We set up the forecasting survey to test if scientists' predictions about the effect sizes and
statistical significance levels (whether p < .05 or not) associated with the different sets of study
materials would be positively correlated with the realized outcomes. Note that in asking
forecasters to predict statistical significance levels, we are not endorsing the idea that something
magical happens at p =.05, or the binary assumption of there being a result if p < .05 and none if
p > .05 (Greenland, 2017). Yet, given that in many fields and journals this criterion is used to
indicate the minimum support required to claim an effect (see McShane & Gelman, 2017), we
find that it is interesting to see whether a crowd of researchers can predict this binary outcome.
In addition, we tested whether monetary incentives or individual characteristics of the
forecasters increased the accuracy of the predictions. The planned analyses for the forecasting
study are detailed at https://osf.io/9jzy4/. Standard errors are clustered at two non-nested levels
in all the regressions employing individual-level data: individual level and team-hypothesis
version level (i.e., the level of a single study). Double clustering renders estimates robust to
potential violations of independence among forecasts generated by the same individual over
different versions of the study materials, and among predictions about the same set of study
materials generated by different researchers.
Overall accuracy. To test our primary hypotheses regarding the accuracy of scientists’
predictions, we examined whether there existed positive correlations between scientists’
forecasts and the estimated effect sizes and statistical significance levels (p < .05 or not) from the
different study versions in the Main Studies, at the team-hypothesis version level. In addition, we
performed paired t-tests on aggregated prediction data and observed effect sizes to test whether
scientists generally underestimated or overestimated the strength of each finding. As
hypothesized, we observed a positive correlation between scientists’ forecasts and the results
CROWDSOURCING HYPOTHESIS TESTS 25
being statistically significant in the predicted direction, r(62) = 0.59, 95% CI [0.40, 0.73], p <
.001. The correlation between scientists’ predictions and the observed effect sizes was likewise
statistically significant: r(62) = 0.71, 95% CI [0.56 ,0.81], p < .001.
[INSERT FIGURES 3A AND 3B ABOUT HERE]
We tested whether scientists underestimated or overestimated the realized outcomes by
employing paired t-tests between the vector collecting the average forecasts and the vectors
collecting the effect sizes and directional statistical significance of each study version.
Descriptively, for both effect sizes and directional statistical significance, predictions and
outcomes were fairly aligned, with no differences reaching statistical significance. For
directional statistical significance in terms of p < .05, the mean of the observed outcomes is M =
0.58 (SD = 0.50) and the mean of the forecasted outcomes is M = 0.48 (SD = 0.09), t(63) = -1.78,
95% CI of the difference of the means [-0.21, 0.01], p = .080. For effect sizes, the mean of the
observed outcomes is M = 0.31 (SD = 0.56) and the mean of the forecasted outcomes is M = 0.25
(SD = 0.10), t(63) = -1.02, 95% CI of the difference of the means [-0.19, 0.06], p = .311.
Evidence from the analysis of the forecasting survey supports the hypothesis that scientists’
predictions are positively correlated with the realized outcomes, both in terms of effect sizes and
in terms of whether the result is statistically significant or not for the different sets of study
materials. Moreover, the analysis shows no evidence of systematic underestimation or
overestimation of the realized outcomes.
Sensitivity to design choices. To test if forecasters were sensitive to how different
versions of the materials designed to test the same hypotheses affect research outcomes, we ran
individual level regressions. These analyses tested whether scientists could predict results within
each hypothesis, rather than only across them. The outcome (realized statistical significance in
terms of p < .05, observed effect size) was the dependent variable and the individual prediction
was the independent variable. As for all other individual level regressions, the standard errors
were clustered at two non-nested levels: individual level (to account for the fact that each
individual made several forecasts) and team-hypothesis version level (to account for the fact that
the forecasts about the same set of materials might possibly be correlated). The model was
estimated with either hypothesis fixed effects (exploiting only the variation in predictions across
teams, as shown in equations (1a) and (1b)) or team fixed effects (exploiting only the variation in
predictions across hypotheses, as shown in (2a) and (2b)).
CROWDSOURCING HYPOTHESIS TESTS 26
(1a) SSth = β0 + β1xith + Hyph + εith
(1b) EEth = β0 + β1x
̂ith + Hyph + εith
The dependent variables SSth and EEth are the realized outcomes, the dummy variable being
positive if the study is statistically significant in (1a), and realized effect size in (1b),
respectively. The independent variables are the individuals’ forecasts, xith for the predictions
regarding statistical significance in terms of p < .05 and x
̂ith for the predictions regarding effect
size. Hyph identify the hypothesis fixed effects, and Teamt are the team fixed effects.
(2a) SSth = β0 + β1xith + Teamt + εith
(2b) EEth = β0 + β1x
̂ith + Teamt + εith
Separately including only hypothesis or only team fixed effects allows us to test if the
forecasts are associated with the realized outcomes using only the variation in forecasts within
hypotheses (making predictions for the different teams within hypotheses) or only the variation in
forecasts within teams (making predictions for the different hypotheses within teams).
The individual prediction coefficient was statistically significant in the expected direction
in both the regressions with only hypothesis fixed effects, and in the regressions with only team
fixed effects. This holds for predicting both statistical significance levels (β1 = .148, t(9018) =
4.07, p < .001 controlling for hypotheses, β1 = .255, t(9007) = 4.38, p < .001 controlling for
teams), and effect sizes (β1 = 0.097, t(9018) = 2.16, p = .031 controlling for hypotheses, β1 =
0.228, t(9007) = 2.68, p = .007 controlling for teams), and shows that forecasters were able to
anticipate results from different teams of materials designers within each hypothesis, as well as
different hypotheses within each team of materials designers. For completeness, we also
estimated the results without any fixed effects (β1 = 0.309, t(9022) = 43.04 for predictions on
whether the result is statistically significant (p < .05) or not, β1 = 0.309, t(9022) = 2.38 for
predictions regarding effect size) and with both team and hypotheses fixed effects (β1 = 0.089,
t(9003) = 2.78 for predictions whether the result is statistically significant (p < .05) or not, β1 =
0.091, t(9003) = 2.77 for predictions regarding effect size), and the individual prediction
coefficient is statistically significant in these models as well (see Tables S5.4a and S5.5 in
CROWDSOURCING HYPOTHESIS TESTS 27
Supplement 5).7 Furthermore, we estimated equations (1a) and (2a) as a probit model (see Table
S5.4b in Supplement 5), obtaining similar results as those obtained using the linear probability
model. In short, scientists were able to predict not only which hypotheses would receive
empirical support (see Figure 3a) but also variability in results for the same hypothesis based on
the design choices made by different research teams (see Figure 3b).
We report several further analyses of the Forecasting Study in Supplement 5, for the
interested reader. In particular, we examine whether monetary incentives increase the accuracy
of forecasts (they do not, at least with the relatively small incentives on offer in this study),
whether characteristics of the forecaster, such as job rank and confidence in their forecasts,
predict accuracy (they do not consistently do so), and repeat our primary analyses for the data
from the Replication Studies and aggregating across the Main Studies and Replication Studies
(the results are similar to those reported here).
How contingent is support for scientific hypotheses on the subjective choices that
researchers make when designing studies? Concerns about the potential dependency of findings
on the stimuli used to capture them have been raised repeatedly (e.g., Baribault et al., 2018;
Campbell & Fiske, 1959; Judd et al., 2012; Monin & Oppenheimer, 2014; Monin et al., 2007;
Wells & Windschitl, 1999). In contrast, the extent to which this problem presents a challenge to
conducting research investigations and interpreting research findings has never been directly
examined. In this crowdsourced project, when up to 13 independent research teams designed
their own studies to test five original research questions, variability in observed effect sizes
proved dramatic, with the Bayesian analyses confirming overwhelming evidence of
heterogeneity for four of five hypotheses and compelling evidence in the fifth case (see
Supplement 7). Descriptively, different research teams designed studies that returned statistically
significant effects in opposing directions for the same research question for four out of five
hypotheses in the Main Studies, and three out of five hypotheses in the Replication Studies (see
Supplement 9). In other words, even when some or most teams created studies that substantiated
7 In all four models, there was a statistically significant association between individual forecasts and outcomes. This
is true for both the predictions regarding whether the study will find a statistically significant effect in the
hypothesized direction and the predictions regarding the realized effect size. Note however that, as the independent
variable (i.e., the individual forecasts) are likely to be measured with error, the estimated coefficients reported in this
paragraph are potentially biased downwards. Measurement error would artificially reduce the correspondence
between forecasts and outcomes, leading to a conservative test of forecaster accuracy.
CROWDSOURCING HYPOTHESIS TESTS 28
a theoretical prediction, at least one other team’s design found the opposite. Even the most
consistently supported original hypotheses still exhibited a wide range of effect sizes, with the
smallest range being d = -0.37 to d = 0.26 (Hypothesis 4, Replication Studies). While the
hypothesis being tested explained substantial variability in effect sizes (i.e., some hypotheses
received more consistent support than others), there remained substantial unexplained
heterogeneity after accounting for the hypothesis being tested, implying that idiosyncratic
choices in stimulus design have a very large effect on observed results, over and above the
overall support (or lack thereof) for the hypothesis in question.
Crowdsourcing makes more transparent the true consistency of support for a scientific
prediction, and provides the opportunity to leverage the collective experience and perspectives of
a crowd of scientists via aggregation (Bates & Granger, 1969; Galton, 1907; Lorenz, Rauhut,
Schweitzer, & Helbing, 2011; Silberzahn et al., 2018; Surowiecki, 2004). Meta-analytically
combining effect sizes across the various conceptual replications yielded overall support for two
of five of the original predictions, and a Bayesian analysis likewise supported two of five
hypotheses. Crowdsourcing hypothesis tests can confirm and disconfirm predictions in a
convincing way, by providing converging evidence across independent investigators who are
unbiased by each other’s approaches or knowledge of the original finding.
Contrary to the “flair” hypothesis (Baumeister, 2016) that some researchers are more
adept at obtaining empirical support for their predictions, none of the 15 different teams involved
in this project designed studies associated with more consistent support for the original ideas.
This non-effect occurred despite variable seniority of team leaders, who ranged from doctoral
students to chaired full professors, with citation counts ranging from zero into the tens of
thousands. The present findings further suggest that replication results are more attributable to
the robustness and generalizability of the original finding than the skill of the scientist carrying
out the replication (whether a direct or conceptual replication; Bench et al., 2017; Open Science
Collaboration, 2015). Although replicating some studies certainly requires specialized technical
knowledge (e.g., of neuroimaging technology), evidence that disappointing reproducibility rates
for published research (e.g., Dewald, Thursby, & Anderson, 1986; Klein et al., 2014; LeBel,
2015; Open Science Collaboration, 2015) are due to a dearth of replicator competence remains
lacking. That said, further meta-scientific work is needed on the role of expertise in replication
results (Tierney et al., 2019b).
CROWDSOURCING HYPOTHESIS TESTS 29
A substantial degree of variability in the results was accounted for by the original
hypotheses themselves, which — as noted earlier — differed in their overall empirical support
(see Figure 1). Although the original effects all replicated using the original materials (when
combining the results of the Main Studies and Replication Studies), three effects were
unsupported overall in the alternative study designs, in some cases returning estimates in the
opposite direction than predicted.
As confirmed in the Bayesian analyses of the project results (Supplement 7), all five
original hypotheses exhibited wide variability in support across different study designs. Although
the present project was able to parse the two, in typical research contexts this heterogeneity in
results due to study design choices co-exists and potentially interacts with heterogeneity in
results due to population differences (McShane et al., 2019; Tiokhin et al., 2018). Discrepant
results and variability in research findings (Open Science Collaboration, 2015; Schweinsberg et
al., 2016) are perhaps unavoidable, and might best be embraced as a normal aspect of the
scientific process. In terms of building solid theory, it may be necessary to vary stimuli and study
designs (Baribault et al., 2018; Caruso et al., 2017; the present initiative), employ a variety of
statistical specifications (Silberzahn et al., in 2018; Simonsohn, Simmons, & Nelson, 2016;
Steegen et al., 2016), and replicate findings across more geographic locations and populations
(Henrich, Heine, & Norenzayan, 2010), before drawing definitive conclusions. With regard to
communicating findings both within and outside the scientific community, more conservative
messaging regarding new research conducted in a single population or relying heavily on a
specific experimental paradigm seems warranted.
Implications for the Five Original Hypotheses
The primary goal of this initiative was to examine effect size dispersion when
independent investigators design studies to address the same research questions. A secondary
purpose was to evaluate the evidence for the five original effects targeted in the crowdsourced
conceptual replications. Below we assess current support and potential future directions for
Hypothesis 1-5, in consultation with the original team that volunteered each research idea for the
Hypothesis 1: Awareness of automatic prejudice. This effect directly replicated using the
original Uhlmann and Cunningham (2000) questionnaire items, with participants in both the
Main Study and Replication expressing overall agreement to the items “Although I don't
CROWDSOURCING HYPOTHESIS TESTS 30
necessarily agree with them, I sometimes have prejudiced feelings (like gut reactions or
spontaneous thoughts) that I don't feel I can prevent”, and “At times stereotypical thoughts about
minorities coming into my head without my necessarily intending them to.” As in the original
data collections by Uhlmann and Cunningham (2000), mean responses to these items were
significantly above the neutral scale midpoint of four (1= strongly disagree, 4= neutral, 7=
strongly agree). At the same time, conceptual replications by different research teams employing
alternative questions failed to confirm the hypothesis that participants so openly self-report
automatic prejudices. Aggregating across the different study designs via meta-analysis reveals no
statistically significant effect in the expected direction, and a Bayesian analysis found moderate
evidence against H1. On reflection, the double-barreled nature of the original items, invoking
both lack of intentions and prejudiced reactions, as well as the use of qualifiers (“I sometimes”,
“At times”) might have biased participants’ responses towards agreement. Further shortcomings
of the original study design are the lack of a relative comparison group (e.g., non-minorities and
members of dominant groups such as White men), and the absence of any probe items regarding
positive or favorable thoughts.
In sum, the present initiative to crowdsource hypothesis tests casts serious doubt on
whether overall endorsement of self-perceived automatic prejudice is generally as high as
initially reported by Uhlmann and Cunningham (2000). Yet, it does not call into question
evidence that different measures of beliefs are correlated at an individual level with scores on
implicit measures of attitude (Hahn et al., 2014) and that awareness of automatic associations can
be experimentally increased (Hahn & Gawronski, 2019). As of yet there are no systematic
reviews or meta-analyses on the empirical relationships between awareness indices and
automatic associations. From the present crowdsourced project, we cannot conclude that
everyday people believe themselves to be as biased as implicit and indirect measures of
automatic associations suggest they are. Indeed, the present results, relying on a wide array of
study designs, suggest they do not generally see themselves as implicitly prejudiced.
Opportunities to improve validated self-report measures of beliefs about one’s automatic
prejudices towards various social groups, and to use them as predictors and outcome measures in
future investigations, remain open.
Hypothesis 2: Extreme offers reduce trust. This crowdsourcing initiative found consistent
evidence for Hypothesis 2 across the range of conceptual replications, as well as in the direct
CROWDSOURCING HYPOTHESIS TESTS 31
replications using the original materials. Both frequentist and Bayesian analyses supported this
particular prediction, with the Bayesian analyses confirming compelling evidence for this
hypothesis despite heterogeneity in estimates across different study designs. This result is
consistent with a recent meta-analysis (Huffmeier, 2014), which found that “hardline”
negotiation tactics (of which extreme first offers are one example) are associated with more
negative “socioemotional” outcomes in negotiations (i.e., perceptions that the hardline negotiator
is unreasonable and uncooperative). However, this meta-analysis did not specifically examine
extreme first offers or trust. Although our findings provide initial support for the idea that
extreme first offers indeed reduce trust on the part of the recipient, that this reduced trust
consequently diminishes information exchange, and value creation remains to be demonstrated.
It also remains unclear to what extent such effects generalize across cultures. Given that
negotiators in some cultural settings may be more accustomed to receiving extreme first offers
than negotiators in other cultural settings, this effect may indeed be culturally moderated. This
possibility is currently being examined in an ongoing international replication project
(Schweinsberg et al., 2019) that will assess the cultural boundary conditions of this effect.
Hypothesis 3: Moral praise for needless work. Earlier findings that Americans morally
praise individuals who continue at their job after coming into sudden wealth were likewise
confirmed by the crowdsourced initiative. Aggregating via meta-analysis across distinct studies
independently created by different research teams, both the frequentist and Bayesian analyses
find compelling evidence in favor of the needless work hypothesis. Although the robustness of
the effect to different operationalizations is now confirmed in two large U.S. samples via the
present host of conceptual replications, the original hypothesis of cross-cultural variability has
yet to be put to a rigorous empirical test. The original research predicted that praise for those
who work in the absence of any material need is steeped in the Protestant work ethic, and hence
should be strongest among those with greater degrees of exposure to U.S. culture (Poehlman,
2007; Uhlmann et al., 2009).
As there is no systematic literature review or meta-analysis on this topic, an ongoing
crowdsourced project by Tierney et al. (2019a) will attempt to directly replicate this and other
original findings regarding work morality across four countries (the United States, the United
Kingdom, Australia, and India). Relying on a “creative destruction” approach to replication, the
Tierney et al. (2019a) initiative will pit the original prediction that moral praise for needless
CROWDSOURCING HYPOTHESIS TESTS 32
work only characterizes U.S. culture against theories positing the general moralization of work
across cultures, regional differences within the United States (i.e., New England vs. other
regions; Fisher, 1989), and valorization of work as a means of personal fulfillment in post-
materialist societies (Inglehart, 1997; Inglehart & Welzel, 2005). Thus, further facets of the
robustness, generalizability, and potential cultural boundedness of this effect remain to be
explored in future research. For now, we conclude that aggregating across the crowdsourced
study designs, the needless work hypothesis is supported for U.S. participants, but the originally
hypothesized moderation by culture (Poehlman, 2007; Uhlmann et al., 2009) remains to be
Hypothesis 4: Proximal authorities drive legitimacy of performance enhancers. The
original finding that the dictates of proximal authorities (e.g., the league, the competitive circuit)
have a larger impact on judgments of the acceptability of using performance enhancing drugs
(PEDs) than the law was not supported in this crowdsourced initiative. Although the finding
directly replicated using the original materials, across a dozen different, independently-
developed study designs, people were not more opposed to the use of PEDs when they are
banned by a proximal authority than when they are illegal, and the Bayesian analysis found
moderate evidence against this hypothesis. This result concurs with follow-up studies done by
the research team who contributed this hypothesis (Landy, Walco, & Bartels, 2017), which were
conducted after this project began. These subsequent studies find that both types of authority
contribute to normative judgments of PED use, to similar degrees. There is currently no
systematic review or meta-analysis of judgments of PED use, but Landy, Walco, and Bartels
(2017) employed an exploratory, “deep-dive” methodology, in which they tested 11 different
potential explanations for opposition to the use of these substances. They concluded that PED
use is opposed for three primary reasons: it violates moral norms of fairness, it poses a risk of
harm to the user, and it tends to violate legitimate conventional rules. The present results help to
clarify this last reason, by showing that the precise source of those rules - the law or a more
proximal authority - does not affect levels of opposition.
Hypothesis 5: Deontological judgments predict happiness. Although the original pattern
of results once again directly replicated using the original materials, the hypothesis that
individuals who tend to make deontological (vs. utilitarian) judgments report different levels of
personal happiness was not supported overall by the crowdsourced conceptual replications.
CROWDSOURCING HYPOTHESIS TESTS 33
Although a statistically significant directional effect in support of H5 was reported in the Main
Studies, the aggregated estimate was close to zero, and the effect did not reach statistical
significance in the Replication Studies. Overall, the Bayesian analysis found strong evidence
against this original prediction. There has not previously been a systematic review or meta-
analysis of the relationship between moral stance and happiness, though prior research has linked
both processes to emotional and intuitive responding (e.g., Everett et al., 2016; Greene, 2013;
Lieberman, 2013; Phillips et al., 2017; Singer, 2005). These results fail to find support for an
association between deontological moral judgments and hedonic happiness that has been
suggested – although not empirically confirmed – by this prior work. Although laypeople appear
to believe that part of what brings happiness is living a moral life (Phillips et al., 2017; Phillips et
al., 2014), adherence to deontological vs. utilitarian ethical principles does not seem to relate to
one’s overall happiness.
Scientists can predict whether a published finding will replicate from the research reports
(Camerer et al., 2016; Dreber et al., 2015) and benchmark findings plus the materials for further
experimental conditions (DellaVigna & Pope, 2018a, 2018b). We find that examination of the
materials for an unpublished study is sufficient for scientists to successfully anticipate the
outcome. In our forecasting survey, predictions by independent scientists were significantly
correlated with both effect sizes and whether the observed results were statistically significant in
the hypothesized direction, and the average predictions were similar to the observed outcomes.
Monetary incentives failed to improve forecasters’ predictive performance. Although
speculative, it is possible that scientists who opted into and completed an extensive survey about
predicting research findings were sufficiently intrinsically motivated to be accurate, so external
incentives did not further increase their motivation (see Lakhani & Wolf, 2005, and Lakhani,
Jeppesen, Lohse, & Panetta, 2007, regarding the tendency for crowdsourced initiatives to
leverage intrinsic motivations). Another potential explanation is that the financial incentives (up
to $60) were not sufficiently strong to affect accuracy.
Comparatively more senior academics (in terms of job rank) were more accurate at
forecasting statistical significance levels (i.e., whether the study’s outcome would be p < .05 in
the predicted direction or not), but not effect sizes (see Supplement 5). Other indices of scientific
eminence, such as number of peer reviewed publications, were unrelated to forecasting accuracy.
CROWDSOURCING HYPOTHESIS TESTS 34
In a separate investigation, DellaVigna and Pope (2018a) found that more senior academics (in
terms of job rank and citations), if anything, underperformed junior academics at predicting how
different incentives would influence the effort and performance of experimental subjects.
Moreover, academics in general did no better than lay people (undergraduates, MBA students,
and MTurk workers) at rank-ordering the effectiveness of different experimental treatments
(DellaVigna & Pope, 2018a). More research is needed on whether traditional indices of scientific
eminence (Sternberg, 2016; Vazire, 2017) are associated with any advantage in designing or
predicting the results of scientific studies.
Unique to the present study, we show that independent scientists are not only able to
predict study results with some success by merely examining the materials, but are also sensitive
to how design choices influence the degree of empirical support for a specific claim. Forecasters
predicted research results with significant accuracy not just across but also within each of the
five hypotheses. This suggests some fine-grained sensitivity to how different operationalizations
of the same hypothesis can impact results. More forecasting surveys and other tools aggregating
beliefs such as prediction markets are needed to determine the accuracy of scientists’ intuitions
about how contextual factors affect research outcomes— for instance, whether scientists are able
to anticipate cultural differences in effects, and whether specializing in research on culture
confers any special advantage. Ongoing projects from our group examine whether academics can
predict the heterogeneity statistics in replication results for prime-to-behavior effects (Tierney et
al., 2019b), differences in replication effect sizes when the same experiment is run in multiple
laboratories (Schweinsberg et al., 2019), and whether findings from the field of strategic
management generalize to other time periods and geographies (Delios et al., 2019).
Limitations and Future Directions
This project represents an early foray into the crowdsourcing of stimulus selection and
study designs (see also Baribault et al., 2018), with important limitations that should be
addressed in future initiatives. The primary meta-scientific purpose of this initiative was to
examine the impact of scientists’ design choices on effect size estimates. Still, a number of
aspects of our approach may have led to artificial homogeneity in study designs. In particular,
materials designers were restricted to creating simple experiments with a self-reported dependent
measure that could be run online in five minutes or less. Further, the key statistical test of the
hypothesis had to be a simple comparison between two conditions (for Hypotheses 1-4), or a
CROWDSOURCING HYPOTHESIS TESTS 35
Pearson correlation (for Hypothesis 5). Full thirty-minute- to hour-long-laboratory paradigms
with factorial designs, research confederates, and more complex manipulations and outcome
measures (e.g., behavioral measures) contain far more researcher choice points and may be
associated with even greater heterogeneity in research results. In addition, the project
coordinators recruited the materials designers from their own social networks, potentially biasing
the project towards demographic and intellectual homogeneity (Ibarra, 1995, 1997). Future
initiatives should recruit materials designers more broadly, to better represent the diversity of
perspectives within a field or subfield (McGuire, 1973; Monin et al., 2007; Duarte, Crawford,
Stern, Haidt, Jussim, & Tetlock, 2015).
Another limitation is that our participants all participated in tests of multiple research
questions. In meta-analysis, it is typically assumed that all samples are independent of one
another, but this assumption is violated in our data. This assumption is not problematic in the
univariate meta-analyses that we present in the main text, but it does complicate the multivariate
meta-analysis we present in Supplement 8. Future crowdsourced initiatives could perhaps assign
each participant to only one research design, or focus exclusively on a single research question,
to avoid these participant-level correlations across hypotheses, which are not accounted for in
our primary analyses. This would allow for a straightforward multivariate meta-analytic
approach, in which participants are nested within designs, which are nested within hypotheses.
Each research team was also free to develop their own dependent measures, which meant that we
could not directly compare raw results across different designs, but could only compare
standardized effect sizes. Future projects in this vein might constrain dependent measures to
allow clean, straightforward comparisons of the effects of multiple, independently developed
This initiative to crowdsource hypothesis tests also targeted only five original hypotheses,
leaving us unable to identify which features of a research idea might be associated with more or
less heterogeneity in study designs and outcomes. Some research ideas may naturally feature a
greater latitude of construal (Beck, McCauley, Segal, & Hershey, 1988; Dunning, Meyerowitz,
& Holzberg, 1989), leading different teams to create more varied experimental paradigms in
order to test them. In the extreme, hypotheses that are theoretically underspecified (unlike the
present H1-H5) may result in a chaos of operationalizations as the materials designers impose
their own priors and assumptions on the idea. Thus, one way to reduce the role of subjective
CROWDSOURCING HYPOTHESIS TESTS 36
researcher choices in research outcomes may be to more fully flesh out the underlying theory at
the outset (Dijksterhuis, 2014; McGuire, 1973; Stroebe & Strack, 2014).
Our limited number of target hypotheses also means one cannot generalize the present
results to all hypotheses in all subfields. We cannot conclude that only 40% of research ideas that
directly replicate will be supported in conceptual replications, or that for the majority of research
questions different designs will return statistically significant effects in opposing directions.
Those are the results of this project only, and further initiatives to crowdsource hypothesis tests
are needed before drawing definitive conclusions about the impact of subjective researcher
choices on empirical outcomes.
Perhaps the most concrete methodological limitation of the present project was the
modest sample of forecasters (N = 141), which reduced the statistical power of the relevant
analyses. Our sample size was comparable to those for prior surveys examining the forecasting
abilities of academics. For example, DellaVigna and Pope (2018a, 2018b) recruited 208
academics for their forecasting research, Dreber et al. (2015) had 47 and 45 active traders in their
two prediction markets for replications, Camerer et al.’s (2016) prediction market had 97
participants, Forsell et al. (in press) included 78 participants, and Camerer et al. (2018) featured
two conditions with 114 and 92 participants in each treatment. For the present project, we
recruited the largest sample we could, given our forecasters’ massive task of reviewing, making
quality assessments, and predicting the results from 64 distinct sets of experimental materials.
Still, the relatively small group of forecasters in our survey indeed limits our conclusions.
Furthermore, there may be overlap between the samples of forecasters included in this and other
studies, as they were recruited by similar methods. Further research is needed using higher-
powered designs, especially with regards to the potential role of forecaster characteristics in
moderating predictive accuracy.
Finally, the crowdsourcing hypothesis tests approach shares certain costs and benefits
with other crowd approaches to scientific research (Uhlmann et al., in press). In comparison to
the standard approach of relying on a small team, recruiting a crowd of collaborators enables big
science, democratizes access to projects, and more effectively assesses the robustness of the
findings. Yet at the same time, crowdsourcing study designs is inefficient, in that for the same
effort and expense, initial evidence for a far greater number of interesting ideas could have been
obtained using a small team or solo investigator approach. In future work, the return on
CROWDSOURCING HYPOTHESIS TESTS 37
investment from crowdsourcing hypothesis tests may be greatest for theoretically important
findings that are well established with a specific paradigm, and whose robustness to alternative
methodological approaches is of general interest.
The present crowdsourced project illustrates the dramatic consequences of researcher
design choices for scientific results. This initiative also provides a roadmap for future
crowdsourced approaches to testing the generality of scientific theories. If a scientific prediction
is theoretically important enough, or has practically significant policy and societal implications,
future investigations could assign it to multiple laboratories to independently operationalize and
carry out empirical tests. The extent to which the results converge (and diverge) across
investigations can then be used to inform discussion and debate, revise theory, and formulate
Scientists craft theories with the ambitious goal of unifying potentially disparate findings
into coherent, generalizable structures of knowledge. This process is often arduous and lengthy
and may be impeded by features of the standard approach to scientific inquiry. Nonetheless, this
process can be streamlined through collective action. As the present investigation demonstrates,
bringing many perspectives and operationalizations to bear on hypotheses provides a richer
account of phenomena than would occur if researchers and teams worked in isolation. Moreover,
we also showed that independent researchers are able to identify not only the hypotheses that are
more likely to be supported by an empirical investigation but also the research designs that,
within a specific hypothesis, are more likely to lead to significant effect. This suggests that
researchers can determine the features of the hypotheses, of the methods, and of the research
designs that are systematically associated with the effect size and the statistical significance of a
research question. Through crowdsourced collaborations such as this one, researchers can craft
theories with more confidence and better understand just how far they extend.
CROWDSOURCING HYPOTHESIS TESTS 38
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CROWDSOURCING HYPOTHESIS TESTS 48
Table 1. Directional and nondirectional formulations of the five hypotheses.
Directional: People explicitly self-report an awareness of harboring negative automatic
associations with members of negatively stereotyped social groups.
Nondirectional: When directly asked, do people explicitly self-report an awareness of
harboring negative automatic associations with members of negatively stereotyped social
Directional: Negotiators who make extreme first offers are trusted less, relative to negotiators
who make moderate first offers.
Nondirectional: Are negotiators who make extreme first offers trusted more, less, or the same
relative to negotiators who make moderate first offers?
Directional: A person continuing to work despite having no material/financial need to work
has beneficial effects on moral judgments of that individual.
Nondirectional: What are the effects of continuing to work despite having no
material/financial need to work on moral judgments of that individual — beneficial,
detrimental, or no effect?
Directional: Part of why people are opposed to the use of performance enhancing drugs in
sports is because they are “against the rules”. But, whether the performance enhancer is against
the rules established by a proximal authority (e.g., the league) contributes more to this
judgment than whether it is against the law.
Nondirectional: Part of why people are opposed to the use of performance enhancing drugs in
sports is because they are "against the rules". But which contributes more to this judgment —
whether the performance enhancer is against the law, or whether it is against the rules
established by a more proximal authority (e.g., the league)?
Directional: The tendency to make deontological (as opposed to utilitarian) judgments is
positively related to personal happiness.
Nondirectional: Is a utilitarian vs. deontological moral orientation related to personal
CROWDSOURCING HYPOTHESIS TESTS 49
Table 2. Effect sizes and Q, I2, and τ2 statistics from meta-analyses of Main Studies and Replication Studies.
Awareness of automatic prejudice
d = 0.07 [-0.22, 0.37]
Q(12) = 897.51***
99.08% [98.20, 99.67]
0.28 [0.14, 0.81]
Extreme offers reduce trust
d = 1.04 [0.61, 1.47]
Q(12) = 568.36***
98.25% [96.58, 99.36]
0.61 [0.31, 1.70]
Moral praise for needless work
d = 0.33 [0.17, 0.50]
Q(12) = 152.45***
93.55% [87.39, 97.68]
0.09 [0.04, 0.26]
Proximal authorities drive legitimacy of
d = 0.07 [-0.05, 0.20]
Q(11) = 89.72***
87.94% [75.82, 95.85]
0.04 [0.02, 0.13]
Deontological judgments predict happiness
r = 0.06 [0.01, 0.11]
Q(12) = 52.91***
75.65% [52.68, 90.62]
0.01 [0.00, 0.02]
Effect Size [95% CI]
I2 [95% CI]
τ2 [95% CI]
Awareness of automatic prejudice
d = -0.07 [-0.33, 0.19]
Q(12) = 773.19***
98.88% [97.81, 99.60]
0.23 [0.12, 0.64]
Extreme offers reduce trust
d = 0.61 [0.32, 0.88]
Q(12) = 372.40***
97.09% [94.34, 98.98]
0.26 [0.13, 0.73]
Moral praise for needless work
d = 0.24 [0.11, 0.38]
Q(12) = 129.49***
91.26% [82.81, 96.85]
0.05 [0.03, 0.16]
Proximal authorities drive legitimacy of
d = 0.03 [-0.06, 0.12]
Q(11) = 47.45***
78.06% [55.84, 92.65]
0.02 [0.01, 0.07]
Deontological judgments predict happiness
r = 0.03 [-0.04, 0.09]
Q(12) = 90.93***
86.39% [73.53, 94.97]
0.01 [0.00, 0.03]
Note. ***p < .001.
CROWDSOURCING HYPOTHESIS TESTS 50
Figure 1a. Forest plot of observed effect sizes (independent-groups Cohen’s ds) for Hypothesis
1. The research question was “When directly asked, do people explicitly self-report an awareness
of harboring negative automatic associations with members of negatively stereotyped social
CROWDSOURCING HYPOTHESIS TESTS 51
Figure 1b. Forest plot of observed effect sizes (independent-groups Cohen’s ds) for Hypothesis
2. The research question was “Are negotiators who make extreme first offers trusted more, less,
or the same relative to negotiators who make moderate first offers?”
CROWDSOURCING HYPOTHESIS TESTS 52
Figure 1c. Forest plot of observed effect sizes (independent-groups Cohen’s ds) for Hypothesis
3. The research question was “What are the effects of continuing to work despite having no
material/financial need to work on moral judgments of that individual - beneficial, detrimental,
or no effect?”
CROWDSOURCING HYPOTHESIS TESTS 53
Figure 1d. Forest plot of observed effect sizes (independent-groups Cohen’s ds) for Hypothesis
4. The research question was “Part of why people are opposed to the use of performance
enhancing drugs in sports is because they are ‘against the rules’. But which contributes more to
this judgment - whether the performance enhancer is against the law, or whether it is against the
rules established by a more proximal authority (e.g., the league)?”
CROWDSOURCING HYPOTHESIS TESTS 54
Figure 1e. Forest plot of observed effect sizes (converted to Cohen’s ds, for comparison to other
hypotheses) for Hypothesis 5. The research question was “Is a utilitarian vs. deontological moral
orientation related to personal happiness?”
CROWDSOURCING HYPOTHESIS TESTS 55
Figure 2. Scatter plot comparing Main Study and Replication effect sizes (Cohen’s ds). Each
point in the scatter plot consists of one of 64 study designs. The continuous segment represents
the fitted line; the dashed segment represents the 45-degree line. H1: Awareness of automatic
prejudice, H2: Extreme offers reduce trust, H3: Moral praise for needless work, H4: Proximal
authorities drive legitimacy of performance enhancers, H5: Deontological judgments predict
CROWDSOURCING HYPOTHESIS TESTS 56
Figure 3a. Correlation between average predicted effect size and observed effect size for each
study design. The continuous segment represents the fitted line; the dashed segment represents y
= x. H1: Awareness of automatic prejudice, H2: Extreme offers reduce trust, H3: Moral praise
for needless work, H4: Proximal authorities drive legitimacy of performance enhancers, H5:
Deontological judgments predict happiness.
CROWDSOURCING HYPOTHESIS TESTS 57
Figure 3b. Correlation between average predicted effect size and observed effect size for each
version of the study materials, separately for each of the five hypotheses. Continuous segments
represent fitted lines; dashed segments represent y = x. H1: Awareness of automatic prejudice,
H2: Extreme offers reduce trust, H3: Moral praise for needless work, H4: Proximal authorities
drive legitimacy of performance enhancers, H5: Deontological judgments predict happiness.
CROWDSOURCING HYPOTHESIS TESTS 58
Appendix: The Crowdsourcing Hypothesis Tests Collaboration
Matúš Adamkovič1, Ravin Alaei2, Casper J. Albers3, Aurélien Allard4, Ian A. Anderson5,
Michael R. Andreychik6, Peter Babinčák7, Bradley J. Baker8, Gabriel Baník7, Ernest Baskin9,
Jozef Bavolar10, Ruud M. W. J. Berkers11, Michał Białek12, Joel Blanke13, Johannes Breuer14,
Ambra Brizi15, Stephanie E. V. Brown16, Florian Brühlmann17, Hendrik Bruns18, Leigh
Caldwell19, Jean-François Campourcy20, Eugene Y. Chan21, Yen-Ping Chang22, Benjamin Y.
Cheung23, Alycia Chin24*, Kit W. Cho25, Simon Columbus26, Paul Conway27, Conrad A.
Corretti28, Adam W. Craig29, Paul G. Curran30, Alexander F. Danvers31, Ian G. J. Dawson32,
Martin V. Day33, Erik Dietl34, Johannes T. Doerflinger35, Alice Dominici36, Vilius Dranseika37,38,
Peter A. Edelsbrunner39, John E. Edlund40, Matthew Fisher41, Anna Fung42, Oliver Genschow43,
Timo Gnambs44,45, Matthew H. Goldberg46, Lorenz Graf-Vlachy47, Andrew C. Hafenbrack42,
Sebastian Hafenbrädl48, Andree Hartanto49, Patrick R. Heck50, Joseph P. Heffner51, Joseph
Hilgard52, Felix Holzmeister53, Oleksandr V. Horchak54, Tina S.-T. Huang55, Joachim
Hüffmeier56, Sean Hughes57, Ian Hussey57, Roland Imhoff58, Bastian Jaeger59, Konrad Jamro60,
Samuel G. B. Johnson61, Andrew Jones62, Lucas Keller35, Olga Kombeiz34, Lacy E. Krueger63,
Anthony Lantian64, Justin P. Laplante65, Ljiljana B. Lazarevic66, Jonathan Leclerc67, Nicole
Legate68, James M. Leonhardt69, Desmond W. Leung70,71, Carmel A. Levitan72, Hause Lin2,
Qinglan Liu73, Marco Tullio Liuzza74, Kenneth D. Locke75, Albert L. Ly76, Melanie
MacEacheron77, Christopher R. Madan78, Harry Manley79, Silvia Mari80, Marcel Martončik7,
Scott L. McLean81, Jonathon McPhetres82,83, Brett G. Mercier84, Corinna Michels43, Michael C.
Mullarkey85, Erica D. Musser86, Ladislas Nalborczyk87,57, Gustav Nilsonne88,89, Nicholas G.
Otis90, Sarah M. G. Otner91, Philipp E. Otto92, Oscar Oviedo-Trespalacios93,94, Mariola Paruzel-
Czachura95, Francesco Pellegrini96, Vitor M. D. Pereira97, Hannah Perfecto98, Gerit Pfuhl99, Mark
H. Phillips100, Ori Plonsky101, Maura Pozzi102, Danka B. Purić66, Brett Raymond-Barker103,
David E. Redman104, Caleb J. Reynolds27, Ivan Ropovik7, Lukas Röseler105,106, Janna K.
Ruessmann43, William H. Ryan90, Nika Sablaturova107, Kurt J. Schuepfer108, Astrid Schütz106,
Miroslav Sirota109, Matthias Stefan53, Eric L. Stocks110, Garrett L. Strosser111, Jordan W.
Suchow112, Anna Szabelska113, Kian Siong Tey5, Leonid Tiokhin114, Jais Troian115, Till
Utesch116, Alejandro Vásquez-Echeverría117, Leigh Ann Vaughn118, Mark Verschoor3, Bettina
von Helversen119, Pascal Wallisch120, Sophia C. Weissgerber121, Aaron L. Wichman122, Jan K.
Woike123,124, Iris Žeželj66, Janis H. Zickfeld125,126, Yeonsin Ahn5, Philippe F. Blaettchen5, Xi
Kang5, Yoo Jin Lee5, Philip M. Parker5, Paul A. Parker5, Jamie S. Song5, May-Anne Very5, Lynn
1University of Presov, 2University of Toronto, 3University of Groningen, 4University of Paris
VIII, 5INSEAD, 6Fairfield University, 7University of Prešov, 8University of Massachusetts,
9Saint Joseph's University, 10Pavol Josef Šafárik University in Košice, 11Max Planck Institute for
Human Cognitive & Brain Sciences, 12Kozminski University, 13Stockholm School of Economics,
14GESIS – Leibniz Institute for the Social Sciences, 15Sapienza University of Rome, 16Texas
CROWDSOURCING HYPOTHESIS TESTS 59
A&M University, 17University of Basel, 18University of Hamburg, 19Irrational Agency,
20Université Clermont Auvergne, 21Monash University, 22 Institute of Sociology, Academia
Sinica, Taiwan, 23University of British Columbia, 24Public Company Accounting Oversight
Board, 25University of Houston–Downtown, 26Vrije Universiteit Amsterdam, 27Florida State
University, 28The University of Texas at Dallas, 29University of Kentucky, 30Grand Valley State
University, 31University of Arizona, 32University of Southampton, 33Memorial University of
Newfoundland, 34Loughborough University, 35University of Konstanz, 36European University
Institute, 37Kaunas University of Technology, 38Vilnius University, 39ETH Zurich, 40Rochester
Institute of Technology, 41Southern Methodist University, 42University of Washington,
43University of Cologne, 44Leibniz Institute for Educational Trajectories, 45Johannes Kepler
University Linz, 46Yale University, 47University of Passau, 48IESE Business School, 49Singapore
Management University, 50Geisinger Health System, 51Brown University, 52Illinois State
University, 53University of Innsbruck, 54Instituto Universitário de Lisboa (ISCTE-IUL), CIS-
IUL, 55University College London, 56TU Dortmund University, 57Department of Experimental
Clinical and Health Psychology, Ghent University, 58Johannes Gutenberg University Mainz,
59Tilburg University, 60University of Massachusetts Dartmouth, 61University of Bath,
62University of Liverpool, 63Texas A&M University-Commerce, 64Université Paris Nanterre,
65Clark University, 66University of Belgrade, 67John Molson School of Business, Concordia
University, 68Illinois Institute of Technology, 69University of Nevada, Reno, 70Baruch College,
City University of New York, 71The Graduate Center, City University of New York, 72Occidental
College, 73Hubei University, 74Magna Græcia University of Catanzaro, 75University of Idaho,
76Loma Linda University, 77University of Western Ontario, 78University of Nottingham,
79Chulalongkorn University, 80University of Milano - Bicocca, 81Walden University,
82Massachusetts Institute of Technology, 83University of Regina, 84University of California,
Irvine, 85University of Texas-Austin, 86Florida International University, 87Univ. Grenoble Alpes,
CNRS, LPNC, 38000, Grenoble, France, 88Karolinska Institutet, 89Stockholm University,
90University of California, Berkeley, 91Imperial College Business School, 92European University
Viadrina, 93Queensland University of Technology (QUT), 94Universidad del Norte, 95University
of Silesia, 96Università degli Studi di Padova, 97LanCog, CFUL, Faculdade de Letras,
Universidade de Lisboa, Alameda da Universidade, 98Washington University in St. Louis, 99UiT
The Arctic University of Norway, 100Abilene Christian University, 101Duke University,
102Università Cattolica del Sacro Cuore, 103University of Roehampton, 104Pacific Lutheran
University, 105Harz University of Applied Sciences, 106University of Bamberg, 107Masaryk
University, 108Miami University, 109University of Essex, 110University of Texas at Tyler,
111Southern Utah University, 112Stevens Institute of Technology, 113Queen's University Belfast,
114Eindhoven University of Technology, 115Istanbul Bilgi University, 116University of Münster,
117University of the Republic, 118Ithaca College, 119University of Bremen, 120New York
University, 121University of Kassel, 122Western Kentucky University, 123Max Planck Institute for
Human Development, 124DIW, Berlin, Germany, 125University of Oslo, 126University of
Mannheim, 127McGill University
CROWDSOURCING HYPOTHESIS TESTS 60
The first through 135th members of the Crowdsourcing Hypothesis Tests Collaboration lent their
expertise as evaluators of study quality and forecasters. The 136th through 144th members of the
Crowdsourcing Hypothesis Tests Collaboration lent their expertise as evaluators of study quality
and forecasters in a pilot version of the Forecasting Study. In addition, all members of the
Crowdsourcing Hypothesis Tests Collaboration, along with the authors whose full names are
listed on the first page, revised and approved the final version of this article.
*The Public Company Accounting Oversight Board, as a matter of policy, disclaims
responsibility for any private publication or statement by any of its economic research fellows,
consultants, or employees.
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