And the Winner Is…? Forecasting the Outcome of Others ...expectations and lay beliefs (e.g., Lord, Lepper, & Preston, 1984; Sanbonmatsu et al., 1998), we further propose that this

Running head: FORECASTING THE OUTCOME OF OTHERS’ COMPETITIVE EFFORTS

And the Winner Is…?

Forecasting the Outcome of Others’ Competitive Efforts

*This article may not exactly replicate the authoritative document to be published at JPSP. It is not the copy of record, Published version will be available at https://www.apa.org/pubs/journals/psp/

Forecasting the Outcome of Others’ Competitive Efforts 1

ABSTRACT

People frequently forecast the outcomes of competitive events. Some forecasts are about

oneself (e.g., forecasting how one will perform in an athletic competition, school or job

application, or professional contest), while many other forecasts are about others (e.g., predicting

the outcome of another individual’s athletic competition, school or job application, or

professional contest). In this research, we examine people’s forecasts about others’ competitive

outcomes, illuminate a systematic bias in these forecasts, and document the source of this bias as

well as its downstream consequences. Eight experiments with a total of 3,221 participants in a

variety of competitive contexts demonstrate that when observers forecast the outcome that

another individual will experience, observers systematically overestimate the probability that this

individual will win. Importantly, this misprediction stems from a previously undocumented lay

belief—the belief that other people generally achieve their intentions—which skews observers’

hypothesis testing. We find that this lay belief biases observers’ forecasts even in contexts in

which the other person’s intent is unlikely to generate the person’s intended outcome, and even

when observers are directly incentivized to formulate an accurate forecast.

Keywords: Selective hypothesis testing, lay belief, forecasts, competitions


Before the 2017 London marathon, people around the world held their breath to see if a

runner named Mary Keitany would break the world record (Ayodi, 2017). Similarly, during the

showing of ABC’s The Bachelor (a reality television dating program), fans placed bets on

whether “America’s sweetheart” Raven Gates would win the final rose. Situations like these—

where people forecast another individual’s competitive outcome—happen frequently in people’s

daily lives across a variety of domains, including entertainment (e.g., predicting the outcome of

reality show competitions), business (e.g., predicting the outcome of an entrepreneur’s attempt to

gain market share), politics (e.g., predicting the outcome of Emmanuel Macron’s presidential

campaign), and legal events (e.g., predicting the outcome of the O.J. Simpson trial). These

predictions can have significant consequences for the forecaster. For example, bettors’

predictions about the outcome of an athlete’s attempt to break a world record guide their betting

decisions. Similarly, when deciding whether to purchase stock in an entrepreneur’s company,

investors predict the outcome that the entrepreneur will experience. Across these contexts,

observers aim to formulate accurate predictions and often benefit from making an accurate

forecast, while having little to no control over the event’s outcome.

Substantial research has documented the processes and biases that govern people’s

forecasts of outcomes that they will personally experience (e.g., Ditto, Scepansky, Munro,

Apanovitch, & Lockhart, 1998; Kunda, 1987; Norem & Cantor, 1986; Showers, 1992). This

research finds that people’s desire to preserve their own self-esteem drives these forecasts

because self-relevant outcomes directly implicate one’s ability and thus influence one’s self-

esteem (Ditto et al., 1998; Kim & Niederdeppe, 2016; Kunda, 1987). But what about the other

side of this phenomenon—the observers of these events? From the sidelines, observers also make

predictions about whether an individual’s competitive efforts will earn him or her trophies, jobs,


promotions, good grades, or market success, and these predictions can have significant

consequences on a wide range of observers’ own behaviors, such as their betting, voting,

investment, and leisure decisions (e.g., whether to tune in to a show, or to search for news reports

about an upcoming athletic event). When a sports commentator polls the audience on their

predictions of the outcome of Mary Keitany’s race, or when a neighbor inquires about the

outcome that Bachelor contestant Raven Gates will experience, how do people formulate their

predictions?

We posit that the motivated reasoning devoted to the preservation and reinforcement of

self-esteem—which guides the hypothesis testing underlying people’s forecasts of their own

outcomes—should not guide observers’ forecasts of the outcomes that self-irrelevant others will

experience because observers by definition are not personally engaged with or implicated in

these events. Instead, we propose that the hypothesis testing underlying people’s forecasts of the

competitive outcomes that another self-irrelevant individual will experience are guided by a

unique determinant that is disconnected from the motivation to protect ones’ own self-esteem—a

previously undocumented (and frequently erroneous) lay belief. We further propose that this lay

belief systematically biases people’s forecasts: In contrast to the objectively low base rate of

success that characterizes many competitions (which are often designed such that only one of

many entrants can achieve their desired outcome), we find that people are biased to predict that

another individual will achieve their desired competitive outcome.1 Below, we review the

literature in forecasting and lay beliefs in which our hypotheses are grounded.

1 Of note, our focus on competitions stems from the ubiquity of other-oriented forecasts in these contexts, and the immediate relevance of these forecasts to everyday decision making. Indeed, competitions often draw the attention of observers to self-irrelevant others and prompt observers to predict the outcomes that these others will experience (Hobson, 2015; Luckner, Schröder, & Slamka, 2008; Spann & Skiera, 2009; Wolfers & Zitzewitz, 2004). We discuss the implications of our theoretical framework for predictions in noncompetitive contexts in the General Discussion.


Forecasting and Hypothesis Testing

When people forecast the future, they engage in a process of hypothesis testing. For

example, when people predict the outcome of an application to a competitive university, they

might consider the hypothesis that the university will admit the applicant, or that the university

will not admit the applicant. Importantly, due to cognitive limitations, people can test only one

hypothesis at a time, and research reveals that the hypothesis (i.e., the possible outcome) that is

considered first biases people’s ultimate judgment (Epley & Gilovich, 2005; Wilson, Houston,

Etling, & Brekke, 1996). This is because when people consider a hypothesis, they engage in

confirmatory hypothesis testing—they seek evidence that confirms this hypothesis, give greater

weight to information that supports this hypothesis, and interpret information in a manner

consistent with this hypothesis (Baron, 1991; Nickerson, 1998; Skov & Sherman, 1986; Snyder

& Campbell, 1980). For example, considering the possibility that a university will admit a

particular applicant leads people to search for, attune to, and process information that confirms

this hypothesis (e.g., academic honors, athletic achievements, and extracurricular leadership

positions). As a result, the hypothesis that people test first biases their ultimate forecast.

Significant research has examined how people forecast the competitive outcomes that

they themselves will experience. Given that the first hypothesis considered has an immense

impact on forecasts, much of this extant research has examined the factors that determine the

hypothesis that people first test when they forecast the outcomes that they themselves will

experience. Rich evidence from this line of inquiry has triangulated one variable—people’s

motivation to protect their own self-esteem—as the key determinant of which hypothesis people

opt to test first when they predict the outcome that they themselves will experience. In other


words, people first consider the hypothesis that they will experience the outcome that best

protects their self-esteem (Crocker & Park, 2004; Leary, 1999; Pyszczynski, Greenberg,

Solomon, Arndt, & Schimel, 2004; Tanner & Carlson, 2009; Weinstein, 1980). This motivation

leads many individuals to first test the hypothesis that they will experience a favorable outcome

(e.g., be admitted to the university of their dreams), and thus predict that they will (Darvill &

Johnson, 1991; Kunda, 1987). For some individuals (e.g., those with high trait anxiety),

however, this self-protection motive leads them to engage in defensive pessimism, causing them

to first test the hypothesis (and subsequently predict) that they will perform poorly, in order to

preemptively defend against a loss of self-esteem if an unfavorable outcome indeed unfolds

(Humberg et al., 2017; Martin, Marsh, & Debus, 2001a; Norem & Cantor, 1986; Reich &

Wheeler, 2016; Showers, 1992). In sum, self-relevant forecasts are predominately driven by

forecasters’ desire to protect their self-esteem. As a result, bias in self-forecasting is alleviated

when people’s self-esteem has been affirmed (Ditto et al., 1998; Harris & Napper, 2005; Kim &

Niederdeppe, 2016).

But what about competitive events that do not implicate the forecaster, in which the

forecaster’s self-esteem is not affected by the competition’s outcome? What hypothesis do

observers first test when they forecast which competitive outcome another individual will

experience?

Forecasting for Others

Although significant research has examined the hypotheses that people naturally test

when forecasting their own outcomes, research has not yet explored the hypotheses that people

naturally test when forecasting the outcomes that another individual will experience. Prior


research examining the process of other-oriented forecasting has largely relied on explicitly

instructing people to test a specific hypothesis. This research finds that explicitly directing

forecasters to consider whether another individual will (or will not) experience a particular

outcome leads forecasters to test the hypothesis that the outcome will (or will not) occur (Bassok

& Trope, 1984; Gibson, Sanbonmatsu, & Posavac, 1997; Snyder, Campbell, & Preston, 1982).

For example, prompting someone to test the hypothesis that an athlete will win a competition

biases the forecaster to test the hypothesis that the athlete will win.

Although this approach of imposing explicit instructions provides clean empirical support

for the impact of hypothesis testing on forecasting, this approach does not provide insight into

what naturally happens—which hypothesis people naturally test when making other-oriented

forecasts. The aim and contribution of the current research, therefore, is to illuminate the

hypothesis that observers naturally generate about the outcome of another individual’s

competitive endeavors, uncover why observers systematically and selectively test this hypothesis

first, and document the consequences of this process of selective hypothesis testing.

As previously noted, people’s limited cognitive resources require them to evaluate one

hypothesis at a time in order to simplify information collection and judgment formation

processes (Epley & Gilovich, 2005; Wilson et al., 1996). Thus, when forecasters predict the

outcome that another individual will experience, they may either first test the hypothesis that this

individual will experience a positive outcome (e.g., will win the competition), or first test the

hypothesis that the individual will experience a negative outcome (e.g., will lose the

competition). Which hypothesis do forecasters naturally test first when predicting the

competitive outcome that another individual will experience?


We theorize that the answer to this question begins with the fact that individuals in

competitions strongly intend to produce a particular outcome—to win. And observers are acutely

aware of this intent. In fact, people infer others’ intentions automatically and nonconsciously

(Aarts, Gollwitzer, & Hassin, 2004; Hassin, Aarts, & Ferguson, 2005; Iacoboni et al., 2005;

Liepelt, Cramon, & Brass, 2008). We theorize that people are not only acutely aware of others’

intent, but that people also have a lay belief about others’ intent—the belief that others generally

achieve their intentions. Because people often first test the hypothesis that is consistent with their

expectations and lay beliefs (e.g., Lord, Lepper, & Preston, 1984; Sanbonmatsu et al., 1998), we

further propose that this “intent-to-outcome” lay belief leads observers to first test the hypothesis

that another individual’s intended outcome to win will indeed occur, leading observers to

overestimate others’ likelihood of success.

An Intent-to-Outcome Lay Belief

Lay beliefs are beliefs about causal relationships that people rely on to understand and

respond to the world around them (Argyle, 2013; Kramer, Irmak, Block, & Ilyuk, 2012; Labroo

& Mukhopadhyay, 2009). There are many reasons why people may hold the proposed intent-to-

outcome belief. Observations of covariation are one possible source of this lay belief (Argyle,

2013; Fisher, 2003). When certain events often covary, people develop the belief that those

events are causally related. As a result, for example, people have a lay belief that healthier food

is less tasty (Raghunathan, Naylor, & Hoyer, 2006), that products placed in more central

positions on retail shelves are more popular (Valenzuela & Raghubir, 2009), and that

contentment causes happiness (Furnham & Cheng, 2000). In a similar vein, the proposed intent-

to-outcome lay belief may originate from the fact that people regularly encounter narratives and


observe contexts in which others’ intent covaries with the outcomes that those others ultimately

experience. For instance, news outlets often feature articles and broadcasts detailing success

stories in which individuals achieve their intentions despite many obstacles. People read more

articles about someone breaking a world record, running a marathon after a debilitating illness,

or winning an Olympic medal than stories of others failing to break a world record, not

completing a marathon, or not winning an Olympic medal. Such frequent exposure to others’

success stories (both inside and outside of competition contexts) may increase the accessibility

and perceived magnitude of the positive covariation between others’ intentions and the outcomes

that others experience, creating an intent-to-outcome lay belief.

There are several other possible sources of this intent-to-outcome lay belief as well. For

example, people often infer others’ intentions from others’ actions (i.e., people often presume

that others’ actions resulted from their intentions, such as inferring that a stranger intends to earn

money when she applies for paid employment; Aarts et al., 2004; Hassin et al., 2005); in a

similar vein, people may deduce others’ intentions ex post by observing the outcomes that others

experience and assuming that others intended those outcomes to unfold. If so, this inferential

process may further contribute to the development and reinforcement of the intent-to-outcome

lay belief.

The positivity offset phenomenon may also undergird the proposed intent-to-outcome lay

belief. The positivity offset literature reveals that observers at baseline generally feel mildly

favorable about neutral unknown others (Gardner, 1996; Holbrook, Krosnick, Visser, Gardner, &

Cacioppo, 2001; Ito & Cacioppo, 2005; Winkielman, Berntson, & Cacioppo, 2001), and that

people at baseline often assume that unknown others have at least mildly favorable

characteristics (De Freitas & Cikara, 2018; Newman, Bloom, & Knobe, 2014). One of these


favorable characteristics could be the ability to achieve intended outcomes; if so, observers’

baseline proclivity to view unknown others’ characteristics mildly favorably could over time

contribute to the general belief that others tend to achieve their intentions. Of course, it is

possible that other processes may also contribute to the intent-to-outcome lay belief; we further

explore these possibilities in the General Discussion.

In sum, the intent-to-outcome lay belief may originate from a rich array of sources,

including people’s observation of covariation, the belief that others’ ex post outcomes reflect

their prior intent, and the phenomenon of positivity offset. Because people often first test the

hypothesis that is consistent with their lay beliefs (e.g., Lord et al., 1984; Sanbonmatsu et al.,

1998), we predict that this intent-to-outcome lay belief leads observers to first test the hypothesis

that another individual will achieve their intended outcome of winning, which in turn leads

observers to systematically overestimate another individual’s likelihood of success. More

formally, we predict the following:

H1: When observers predict the competitive outcome that an individual will experience,

observers selectively test the hypothesis that this individual will win.

H2: This selective hypothesis testing biases observers’ forecasts by leading them to

overestimate the likelihood that this individual will win.

H3: This selective hypothesis testing, and the resulting forecasting bias, is driven by an

intent-to-outcome lay belief: the belief that others’ intended outcomes generally occur.

Importantly, this proposed mechanism (H3) is unique to other-oriented forecasts because

it suggests that an intent-to-outcome lay belief determines the hypothesis that observers test (i.e.,

by uniformly leading observers to test the hypothesis that others will achieve their intentions). In


contrast, prior research reveals that an existential self-esteem-protection motive determines the

hypothesis that people test when they predict their own future outcomes, and that this existential

motive can lead different people to test different hypotheses depending on their trait anxiety,

negative affectivity, and task orientation (Ditto et al., 1998; Kim & Niederdeppe, 2016; Kunda,

1987; Martin, Marsh, & Debus, 2001b; Norem & Illingworth, 1993). For example, consider

people’s forecasts for the outcome of a competition that they themselves have entered, versus the

outcome of a self-irrelevant competition that another individual has entered. In the former case,

prior research suggests that people first test the hypothesis that they will attain the outcome that

most protects their self-esteem: Individuals differ in whether this existential motivation drives

them to employ optimism or defensive pessimism when selecting the hypothesis they first test,

and whether they consequently overestimate the likelihood that they will either win or lose,

respectively (Ditto et al., 1998; Humberg et al., 2017; Kim & Niederdeppe, 2016; Kunda, 1987).

However, when observers’ self-esteem is not implicated in the outcome that another individual

will experience in a competition, we propose that this self-esteem-protection motive does not

determine the hypothesis observers selectively test; rather, we theorize that an intent-to-outcome

lay belief uniformly leads observers to selectively test the hypothesis that another individual will

achieve their intended competitive outcome.

Of note, this intent-to-outcome lay belief may be accurate (and thus adaptive) in

environments in which others have high or full control over the outcome they pursue. For

example, when someone intends to purchase a camera, often that person’s intention translates

into that person buying a camera; similarly, when someone intends to watch a movie, often that

person’s intention results in that person watching a movie. However, applying this lay belief to

probabilistic contexts like competitions could generate biased and often inaccurate forecasts.


While people who enter a competition generally desire to win (Deutsch, 1949; Huang, Etkin, &

Jin, 2017; Johnson & Johnson, 1974), the design of many competitions dictates that only one or a

few can achieve their intended outcome, preventing most contestants from achieving their

intended outcome. For instance, while over 2,000 Olympic athletes will enter the 2020 Olympics

with the intention of earning a medal, only a fraction of them will actually succeed in achieving

this intent. A successful outcome in a competition is thus often objectively unlikely, which

renders the intent-to-outcome lay belief inaccurate and misleading in these contexts. As a result,

we predict that the hypothesis testing that is guided by the intent-to-outcome lay belief often

generates erroneous forecasts.

Research Overview

We test our predictions across eight studies. Studies 1A–1C employ two different

measures of selective hypothesis testing to provide converging evidence that, without explicit

instructions, observers selectively test the hypothesis that another individual’s competitive

efforts will succeed (H1). Studies 2A and 2B provide additional evidence for this proposition and

find that this selective hypothesis testing biases people to overestimate another individual’s

likelihood of winning a competition (H1 and H2). Studies 3 and 4 dive deeper into the proposed

mechanism of the intent-to-outcome lay belief (H3). First, Study 3 finds that people hold the

proposed intent-to-outcome lay belief, and that directly weakening the intent-to-outcome lay

belief attenuates the forecasting bias. Study 4 examines and rules out an alternative explanation

for the bias. Finally, Study 5 shows that the bias persists even when forecasters are incentivized

to make accurate predictions, and even when the base rate probability that a contestant will

achieve his or her intention is low.


In these studies, we leverage multiple competitive contexts (logo competitions,

photography competitions, golf tournaments, and slogan competitions), and examine forecasts of

the competitive outcomes that will be experienced by a diverse range of individuals belonging to

different social groups (i.e., individuals with different genders, ethnicities, ages, and disability

statuses). Across these diverse competitions and competitors, we find that when observers

forecast the competitive outcome that another individual will experience, they spontaneously and

selectively test the hypothesis that the individual will win, which in turn produces a previously-

undocumented bias in observers’ forecasts.

Study 1A: Measuring Selective Hypothesis Testing via Thought Listing

Study 1A provides a first test of our prediction that when observers forecast the outcome

that another individual will experience in a competition, observers selectively test the hypothesis

that this individual will win. To test this proposition, we leveraged a paradigm validated in prior

literature to uncover the hypothesis that people naturally test (e.g., Bassok & Trope, 1984;

Gibson et al., 1997; Snyder et al., 1982). Specifically, we randomly assigned participants to one

of three conditions: Participants were instructed to consider the hypothesis that a contestant

would achieve his intended outcome and win, instructed to consider the hypothesis that a

contestant would not achieve his intended outcome and lose, or given no instructions. This last

condition illuminates the hypothesis that people naturally test: A classic indicator of whether

people selectively test a particular hypothesis naturally is when people’s natural hypothesis

testing (i.e., the hypothesis testing that people conduct when they are given no explicit


instructions) is similar to that of individuals who are explicitly instructed to test that hypothesis

(Bassok & Trope, 1984; Gibson et al., 1997; Snyder et al., 1982).

In Study 1A we also employed a measure that prior literature has validated to capture

selective hypothesis testing: thought listing (Posavac, Kardes, & Brakus, 2010; Raju, Unnava, &

Montgomery, 2009). Prior research has found that the thoughts that people report having when

formulating a prediction illuminate the hypothesis that they are selectively testing. For example,

people who are tasked with testing the hypothesis that a particular product is of high (vs. low)

quality report having more thoughts about information consistent with the product being of high

quality than do participants tasked with testing the hypothesis that the product is of low quality;

as a result, thought patterns indicate the hypothesis that people are testing (Posavac et al., 2010;

Raju et al., 2009).

We predicted that the thought pattern of individuals who were explicitly instructed to test

the hypothesis that a contestant will achieve their intended outcome of winning would closely

match the thought pattern of unprompted participants; we further predicted that participants in

both of these conditions would think more about information supporting the conclusion that the

contestant would win than would participants who were instructed to consider the hypothesis that

the individual would not achieve his intentions (i.e., lose). These predictions (in this study and in

all subsequent studies) were developed a priori.2

2 The analyses employed to test our predictions were also developed a priori in all studies, with one exception: In Studies 1B–2A, the distribution of the selective-hypothesis-testing data (which were proportion data) prevented us from implementing our a priori analysis plan (which was to analyze the selective-hypothesis-testing data in each study with an ANOVA). Specifically, as described in Studies 1B–2A, Levene’s tests revealed that the selective-hypothesis-testing data in these studies violated the homogeneity of variance assumption of the ANOVA. Therefore, in these studies we instead employed a binomial logistic regression, which is the recommended analysis for proportion data that violate the homogeneity of variance assumption (because a binomial logistic regression makes no assumptions about the shape or width of the distribution of the selective-hypothesis-testing data in each condition; Agresti, 2010; Anderson & Braak, 2003; Bonnini, Corain, Morozzi, & Salmaso, 2014; Hosmer, Lemeshow, & Sturdivant, 2013; Liu & Agresti, 2005).


Method

Two hundred ten participants (mean age = 19 years; 51% male) from an East Coast

university participated in an in-lab study in exchange for course credit. Students participated in

this study during a larger study session in which they completed unrelated surveys from various

researchers; given limited access to this participant pool, we collected responses from all the

students who signed up to complete this lab session (rather than determining an a priori sample

size). We determined this stopping rule a priori (before data collection began). A sensitivity

power analysis with a data simulation approach3 indicated that the resulting sample of 210

participants provided 80% power to detect an effect (with an ANOVA) for the difference

between the planned contrasts of Cohen’s d = .51 and 60% power to detect an effect of Cohen’s

d = .38. For reference, the estimated median effect size in social psychological research is about

d = .38 (Lovakov & Agadullina, 2017).

In Study 1A, all participants read about four professional graphic designers, named Stan

Richard, Mark John, Craig Darcy, and Tim Barry, who designed and submitted a logo for a new

coffee shop that would soon open on the students’ campus. Participants learned that the new

coffee shop had decided that students would vote for the logo they liked best, and that the coffee

shop would use the winning logo. Participants further read that voting would take place later in

the semester, and that the coffee shop would give the graphic designer who designed the winning

logo a large cash prize.

Participants read that each student participating in this survey would be randomly

3 This simulation procedure conducted 1,000 simulations of three conditions (in which each condition had a sample size of 70, resulting in a total sample size of 210) sampled from randomly-generated proportion data, and computed the minimum effect size that could be detected (with an ANOVA) for the paired comparisons with this sample size.


assigned to think about the outcome of the competition for one of the graphic designers, and all

participants read that they had been assigned to think about what the outcome would be for Stan

Richard. Participants then viewed each of the logos that the four designers submitted (see

Supplementary Materials A). These logos were pretested to ensure that they were equally liked.4

Participants were then randomly assigned to one of three conditions. Participants in the

Natural condition, which captured the hypothesis that participants naturally tested, did not

receive any further instruction. In contrast, participants in the Intended (vs. Unintended)

Outcome condition were directed to consider what Stan intends to do in the competition, and

then to think about the reasons why Stan’s intended outcome might (vs. might not) occur (i.e.,

why Stan might win (vs. might lose)).5

Next, participants in all conditions were asked what they thought the outcome of the

competition would be for Stan. As they contemplated their forecast, all participants were asked

to list all of their thoughts (procedures adapted from Cacioppo, von Hippel, & Ernst, 1997) and

to advance to the next screen when they had completed deliberating about their forecast. On the

ensuing screen, we presented participants with the thoughts they had listed and asked them to

select one of three options for each thought: (1) “The thought relates to information that suggests

that Stan’s logo will win”; (2) “The thought relates to information that suggests that Stan’s logo

will lose”; (3) “The thought relates to information that is neutral about whether Stan’s logo will

win or lose.” Following prior research (Haugtvedt & Wegener, 1994; Leippe & Elkin, 1987;

4 In a pretest, 201 participants drawn from the same pool (mean age = 20; 49% male) viewed the logos and were randomly assigned to indicate how much they liked one of the logos on a 7-point scale (1: Not at all; 7: Very much). An ANOVA revealed that participants liked the four logos equally (MStan = 4.73, SDStan = 1.47; MCraig = 4.90, SDCraig = 1.38; MMark = 5.22, SDMark = 1.49; MTim = 5.06, SDTim = 1.13, F(3, 197) = 1.20, p = .310). 5 Prior literature finds that people believe that others intend to win (rather than lose) the competitions that they enter (Deutsch, 1949; Huang, Etkin, & Jin, 2017; Johnson & Johnson, 1974). Thus, for ease of exposition, in the remainder of our discussion of Study 1A, we refer to Stan’s intended outcome as winning, and Stan’s unintended outcome as losing.


Shipherd & Beck, 1999), we computed a selective-hypothesis-testing index by dividing the

number of thoughts related to information suggesting that Stan’s logo would win by the total

number of thoughts that participants generated. Higher values thus reflected a greater relative

frequency of thoughts about the possibility that Stan would win.

Results and Discussion

To determine whether we could implement our a priori analysis plan of employing an

ANOVA to analyze the selective-hypothesis-testing data, we first evaluated whether the data’s

variance was homogenous by conducting the Levene’s test, in which a significant result would

indicate that the variance was heterogeneous. This analysis revealed that the data’s variance was

homogenous (F(2, 207) = 2.52, p = .083;6 Brown et al., 1996; Glass, 1966; Levene, 1960;

Stephens, Markus, Townsend, & 2007; Walton, Cohen, Cwir, & Spencer, 2012). After

determining that the variance of the selective-hypothesis-testing index was homogenous, we next

turned our attention to our primary hypothesis—the condition’s impact on selective hypothesis

testing.

An ANOVA of condition on the selective-hypothesis-testing index revealed that

condition significantly affected participants’ hypothesis testing, F(2, 207) = 9.24, p < .001.

Consistent with prior research (e.g., Gibson et al., 1997), planned contrasts revealed that

6 As previously described, we analyzed the selective-hypothesis-testing data with an ANOVA because the Levene’s test revealed that the data’s variance was homogenous. However, because the Levene’s test was marginal (F(2, 207) = 2.52, p = .083), we conducted supplemental analyses to ensure Study 1A’s results were robust when the data were analyzed using a binomial logistic regression, where there is no assumption of homogeneity of variance. This robustness check revealed that Study 1A’s results persisted (in both pattern and significance): A binomial logistic regression revealed that participants in the Intended Outcome condition had a greater proportion of positive thoughts about the possibility that Stan would win than did participants in the Unintended Outcome condition (B = 1.32, SE = .37, z = 3.59, p < .001). In addition, participants in the Natural condition also had a greater proportion of positive thoughts about the possibility that Stan would win than participants in the Unintended Outcome condition (B = .93, SE = .36, z = 2.56, p = .010), and a similar proportion of positive thoughts related to the possibility that Stan would win as did participants in the Intended Outcome condition (B = .39, SE = .33, z = 1.16, p = .245).


participants in the Intended Outcome condition (M = .61, SE = .43) had a greater proportion of

thoughts about the possibility that Stan would win than did participants in the Unintended

Outcome condition (M = .29, SE = .43; Fisher’s LSD: p < .001; Cohen’s d = .735, 95% CI [.383,

1.086]). Providing initial support for our theorizing, participants in the Natural condition (M =

.51, SE = .44) also had a greater proportion of thoughts about the possibility that Stan would win

than did participants in the Unintended Outcome condition (Fisher’s LSD: p = .004; Cohen’s d =

.504, 95% CI [.158, .850]), and a similar proportion of thoughts related to the possibility that

Stan would win as did participants in the Intended Outcome condition (Fisher’s LSD: p = .182;

Cohen’s d = .220, 95% CI [−.106, .546]).

Because people’s thoughts reflect the hypothesis that they are selectively testing (Posavac

et al., 2010; Raju et al., 2009), these results provide initial evidence that observers naturally

generate and primarily test the hypothesis that an individual will achieve his or her intended

outcome of winning when making other-oriented forecasts: Despite the fact that the four logos

were rated as equally likable (see pretest in footnote 4), participants naturally thought about

information relating to Stan achieving his intended outcome of winning to a similar extent as

participants who were explicitly instructed to test this hypothesis, and more so than participants

who were instructed to test the hypothesis that Stan would not achieve his intended outcome (i.e.,

lose). Of note, this spontaneous selective hypothesis testing emerged despite the fact that the

base rate probability of any particular logo winning was relatively low (i.e., 25%). In Study 1B,

we aimed to provide converging evidence for these findings using a different measure of

selective hypothesis testing, a different competitive context, a contestant from a different social

category, and a different participant pool.


Study 1B: Measuring Selective Hypothesis Testing via Information Search

In Study 1B we captured selective hypothesis testing by measuring information seeking.

When forecasters selectively test the hypothesis that an outcome will (vs. will not) occur, they

seek a greater proportion of information that supports the hypothesis that it will (vs. will not)

occur (e.g., Bassok & Trope, 1984; Nickerson, 1998; Snyder & Campbell, 1980); the

information that people seek thus illuminates the hypothesis that they are selectively testing. We

predicted that unprompted participants would naturally seek a similar proportion of information

supporting the conclusion that a contestant would achieve their intended outcome of winning as

would participants who were explicitly prompted to consider that hypothesis, and that these two

groups would seek a larger proportion of this information than would participants prompted to

consider the hypothesis that the contestant would not achieve their intention (i.e., lose).

Study 1B also addressed three limitations in Study 1A. First, in Study 1A, participants

forecasted the competitive outcome of an individual named Stan—a name that might have

implied that the individual was a White male. Because people often perceive White males as

highly competent and agentic (Cuddy, Fiske, & Glick, 2008), it is possible that these group-

specific stereotypes underlie the biased hypothesis testing documented in Study 1A. If they do,

then the same bias documented in Study 1A would not emerge when people forecast the

competitive outcome of an individual who belongs to a group that is not stereotypically

perceived as highly competent and agentic. Therefore, in Study 1B we investigated whether

these group-specific stereotypes may underlie the current phenomenon by examining

participants’ predictions of the competitive outcome of an individual who is not stereotypically

perceived as highly competent and agentic—an elderly individual (Cuddy et al., 2008;


Fernández-Ballesteros et al., 2016; Thompson & Ince, 2013). To further enhance

generalizability, this individual was also female (in contrast to the male contestant in Study 1A).

Because we theorize that an intent-to-outcome lay belief (rather than group-specific stereotypes)

underlies forecasts of others’ competitive outcomes, we predicted that the same bias documented

in Study 1A would also emerge in Study 1B’s forecasting context.

Second, to rule out any potential primacy effects, in Study 1B we counterbalanced the

order in which participants read about the individual whose outcome they forecasted versus the

individual’s opponent. Our theorizing predicts that the biased hypothesis testing documented in

Study 1A should occur regardless of whether people forecast the competitive outcome for an

individual they read about first or second.

Third, Study 1B further tested the generalizability of the bias documented in Study 1A by

examining whether it persists when forecasters learn about contestants’ historical competitive

outcomes. In Study 1A, participants had complete information about the contestants’

submissions (i.e., all participants viewed the logo that each contestant submitted to the

competition), but did not have information about the contestants’ past competitive record. This

forecasting context mirrors the numerous contexts in which people in the real world similarly

forecast others’ outcomes without historical performance information (e.g., as occurs when

reality show fans forecast the outcome that first-time contestants will experience, when

publishers forecast the outcome that first-time novelists will experience, etc.). However, there are

also situations in which people forecast the outcomes of contestants who have a past competitive

record, and forecasters often view this information when formulating their forecasts in such

contexts. Our theorizing predicts that the biased hypothesis testing documented in Study 1A


should persist when forecasters view contestants’ past competitive record; therefore, Study 1B

tests whether the bias indeed persists in such contexts.

Method

Three hundred participants (mean age = 37 years; 38% male) from Mechanical Turk

participated in an online study in exchange for 30 cents. We aimed to recruit 100 participants per

condition in Study 1B and Studies 2–5 following prior research investigating lay beliefs (e.g.,

Hannikainen, 2018; Klinger, Scholer, Hi, & Molden, 2018; La, Louis, Hornsey, & Leonardelli,

2016). A sensitivity power analysis with the same previously-described data simulation approach

(i.e., 1,000 simulations of three conditions with 100 participants each) indicated that a sample of

this size would provide 80% power to detect an effect (with an ANOVA) for the difference

between the planned contrasts of Cohen’s d = .41 and 60% power to detect an effect of Cohen’s

d = .33. We adhered to this target sample size in all of our studies in the manuscript except for

Study 1C, in which we investigated an attenuation interaction. In this moderation study, we

followed the recommendations outlined by Simonsohn (2014) to double the cell size; therefore,

we recruited 400 participants per condition. A sensitivity power analysis with a data simulation

approach indicated that a sample of this size would provide 80% power to detect an effect (with

an ANOVA) for the difference between three conditions of Cohen’s d = .19 and 60% power to

detect an effect of Cohen’s d = .17. We determined these stopping rules in all studies a priori

(before data collection began).

In Study 1B, participants read about two photographers who entered a photography

competition—Beth Smith and Pearl Jones. Participants read that both Beth and Pearl are retired


and live in retirement homes.7 Importantly, we counterbalanced the order in which participants

read the women’s names (i.e., “Beth Smith and Pearl Jones” or “Pearl Jones and Beth Smith”)

throughout the experimental session.

All participants read that Beth and Pearl submitted a photograph into an upcoming

photography competition, and that the participants’ task was to view the photographs and to

predict the outcome of the competition (which participants read would be determined later in the

week by other Mechanical Turk participants). Participants then viewed the photographs that the

two women submitted to the competition (Supplementary Materials B),8 as well as information

about Beth’s and Pearl’s outcomes in prior competitions in which they had competed—

participants read that both Beth and Pearl had competed in numerous photography competitions

over the last 10 years in which contestants submitted photographs of numerous natural

landscapes (i.e., of trees, streams, the sky, and the ocean), and participants viewed the number of

times that Beth and Pearl had won these competitions (Supplementary Materials B).

Next, participants read that each participant in the survey had been randomly assigned to

think about what the outcome of the competition would be for either Beth or Pearl. We

counterbalanced whether participants next read that they had been randomly assigned to predict

the outcome of the competition for either Beth or Pearl.

7 In order to verify that this manipulation successfully led participants to perceive the photographers as elderly, we captured participants’ perceptions of whether each photographer was elderly at the end of the survey. The majority of participants indicated that both Beth (89.7%; χ2(df = 1, N = 300) = 190.73, p < .001; Cohen’s d = 2.614, 95% CI [2.240, 2.988]) and Pearl (91.0%; χ2(df = 1, N = 300) = 201.72, p < .001; Cohen’s d = 2.832, 95% CI [2.438, 3.226]) were elderly. A robustness check revealed that Study 1B’s results persisted (in both pattern and significance) when analyses included only the majority of participants who believed that Beth and Pearl were elderly. 8 In a pretest, 200 participants drawn from the same pool (mean age = 38; 47% male) viewed the photographs, and were randomly assigned to indicate their liking of one of the photographs. Participants indicated their responses on a 7-point scale (1: Not at all; 7: Very much). A t-test revealed that participants liked the two photographs equally (MBeth’s

Photograph = 3.91, SDBeth’s Photograph = 1.62; MPearl’s Photograph = 3.80, SDPearl’s Photograph = 1.52; t[198] = .49, p = .626; Cohen’s d = .069, 95% CI [−.209, .347]).


Participants were then also randomly assigned to one of three conditions. Participants in

the Natural condition did not receive any further instructions. In contrast, participants in the

Intended (Unintended) Outcome condition were instructed to consider what the individual whose

outcome they were forecasting intends to do in the competition, and then to think about the

reasons why her intended outcome might (might not) occur.

Next, we assessed participants’ information-seeking behavior: Participants read that to

help them make their prediction, they could look at three comments that other Mechanical Turk

workers had made about the photographer whose outcome they were forecasting during other

competitions in which she had previously participated. Participants chose between six comments,

three of which were consistent with the hypothesis that she would win the competition, and three

of which were consistent with the hypothesis that she would lose the competition. Each comment

was labeled with a brief summary of its content (see Supplementary Materials B). Importantly,

the topics of these comments were held constant across the comments’ valence, and we simply

varied whether each comment was positive or negative with respect to the individual’s

photography.

Participants selected the radio button next to each of the three comments that they wished

to view. We calculated an information-seeking index by dividing the number of positive pieces

of information that participants selected to view by the total number of pieces of information that

participants selected.


A Levene’s test revealed that the information-seeking index violated the ANOVA’s

homogeneity of variance assumption, F(2, 297) = 4.83, p = .009). Therefore, we employed a


binomial logistic regression to analyze the information-seeking index.9 As expected, this

regression revealed that there was no interaction between condition and order on information

seeking, Bs £ .81, SEs £ .88, zs £ .92, ps ³ .358; thus, we collapsed across order to explore the

effect of condition on information seeking.

A binomial logistic regression of condition on the information-seeking data revealed that

participants in the Intended Outcome condition (M = .70, SE = .03) sought a greater proportion of

positive information than participants in the Unintended Outcome condition (M = .52, SE = .03;

B = .76, SE = .29, z = 2.59, p = .009; Cohen’s d = .594, 95% CI [.305, .884]). Supporting our

theory, participants in the Natural condition (M = .70, SE = .03) also sought a greater proportion

of positive information than participants in the Unintended Outcome condition (B = .77, SE =

.30, z = 2.56, p = .010; Cohen’s d = .580, 95% CI [.296, .864]), and a similar proportion of

positive information as participants in the Intended Outcome condition (B = .01, SE = .31, z =

.03, p = .979; Cohen’s d = .006, 95% CI [−.270, .282])

In sum, Study 1B provides converging evidence that when people predict an individual’s

competitive outcome, they naturally test the hypothesis that the individual will win. Participants

naturally sought a similar proportion of information consistent with the hypothesis that an

individual’s intended outcome of winning would occur as did participants who were explicitly

instructed to test that hypothesis. Importantly, Study 1B indicates that this phenomenon occurs

even when the contestant whose outcome participants forecasted is elderly (i.e., even when the

9 Because we analyzed Study 1B’s data with a binomial logistic regression, we conducted an additional sensitivity analysis for the binomial logistic regression. Specifically, this sensitivity analysis was computed via a simulation procedure which conducted 1,000 simulations of three conditions (in which each condition had a sample size of 100) sampled from randomly-generated proportion data, and computed the minimum effect size that could be detected (with a binomial logistic regression) for the paired comparisons with this sample size. This analysis indicated that the sample size employed in Study 1B provided 80% power to detect an effect (with a binomial logistic regression) for the difference between the planned contrasts of Cohen’s d = .42 and 60% power to detect an effect of Cohen’s d = .29.


individual is not stereotypically perceived as highly competent; Cuddy et al., 2008; Fernández-

Ballesteros et al., 2016; Thompson & Ince, 2013), and regardless of the order in which

participants learned about the two contestants.

Study 1C: Documenting A General Bias

Study 1C provided a second test of our theorizing that the documented bias is driven by a

general intent-to-outcome lay belief (rather than group-specific stereotypes). To that end, Study

1C examined whether the bias documented in the prior studies transcends racial boundaries.

Whereas the contestants’ names in the previous studies might have implied that the contestants

were White, Study 1C examined whether people naturally test the hypothesis that an individual

will attain their intended competitive outcome when the individual’s name implies that they are

African American. Substantial literature reveals that others’ race heavily shades people’s

perceptions of others’ characteristics, others’ behaviors, and the consequences of those others’

behaviors (e.g., Abreu 1999; Ford, 1997; Hurwitz & Peffley, 1997; Plant, Goplen, & Kunstman,

2011; Sagar & Schofield, 1980); as a result, examining whether the proposed phenomenon

transcends racial boundaries is an important test of our theorizing that the proposed phenomenon

does not hinge on group-specific stereotypes.

Study 1C also assessed three additional alternative accounts. First, it is possible that the

proposed effect is driven by a promotion (vs. prevention) orientation. Promotion focus increases

people’s attention to hope, aspiration, and accomplishment (Crowe & Higgins, 1997; Higgins &

Silberman, 1998); thus, if promotion focus underlies other-oriented forecasts, individuals who

have a high dispositional promotion focus should be particularly likely to forecast that others will


succeed (e.g., Jain, Agrawal, & Maheswaran, 2006; Liberman, Molden, Idson, & Higgins, 2001).

In addition, it is possible that the proposed effect occurs because people feel empathic concern

for others, which prompts a desire for others to succeed. To investigate this possibility, we

measured participants’ trait empathy because substantial research reveals that when empathic

concern underlies particular judgments, individuals with high trait empathy are more likely to

form those judgments (e.g., Darling, Nandy, & Breazeal, 2015; Sonnby-Borgström, Jönsson, &

Svensson, 2003; Waung & Highhouse, 1997). In a similar vein, we also measured participants’

trait optimism to examine whether optimism underlies the observed effect (e.g., Stankevicius,

Huys, Kalra, & Seriès, 2014). Because people primarily make optimistic judgments about self-

relevant outcomes rather than non-self-relevant outcomes (e.g., Weinstein, 1980), we did not

expect optimism to play a driving role in shaping other-oriented forecasts; nevertheless, we

investigated this possibility in Study 1C.

Method

Twelve hundred participants (mean age = 37 years; 42% male) from Mechanical Turk

participated in an online study in exchange for 60 cents. The procedures were identical to those

in Study 1B, with two exceptions. First, all participants in Study 1C read that the photographers’

names were Lakisha Smith and Tamika Jones, which are prototypically African American

female names (Bertrand & Mullainathan, 2004).10 As in Study 1B, the particular photographer

10 Of note, employing prototypically African American names is a validated and frequently used method of conveying that a particular individual is African American (Bertrand & Mullainathan, 2004; Butler & Broockman, 2011; Cotton, O’Neill, & Griffin, 2008; King, Mendoza, Madera, Hebl, & Knight, 2006). Nevertheless, in order to ensure that participants indeed inferred that the photographers were African American, we queried participants for their perceptions of the two photographers’ race at the end of the survey. The majority of participants indicated that both Lakisha (82.0%; χ2(df = 1, N = 1200) = 491.5, p < .001; Cohen’s d = 1.665, 95% CI [1.518, 1.813]) and Tamika (87.4%; χ2(df = 1, N = 1200) = 672.00, p < .001; Cohen’s d = 2.255, 95% CI [2.085, 2.426]) were African American.


whose outcome participants read that they had been randomly assigned to forecast was

counterbalanced between participants, as was the order in which participants read the

photographers’ names throughout the experimental session (i.e., “Lakisha Smith and Tamika

Jones” or “Tamika Jones and Lakisha Smith”). Participants were then randomly assigned to

either the Natural, the Intended Outcome, or the Unintended Outcome condition (which were

manipulated as in Study 1B), and we assessed participants’ information-seeking behavior via the

same procedures described in Study 1B.

Next, we assessed participants’ dispositional empathy by administering the

Multidimensional Emotional Empathy Scale (Caruso & Mayer, 1998). The scale contains 30

items, and includes statements such as “The suffering of others deeply disturbs me” and “I

don’t give others’ feelings much thought” (reverse-coded; see Supplementary Materials C for

the full scale). Participants indicated the extent to which each statement characterized them on a

5-point scale (1: Strongly Disagree; 5: Strongly Agree). Responses were averaged to create a

composite dispositional empathy score (α = .93).

In addition, we assessed participants’ dispositional optimism and pessimism by

administering the Life Orientation Test Revised Scale (LOT-R; Scheier, Carver, & Bridges,

1994). The LOT-R contains three items assessing trait optimism (e.g., “In uncertain times, I

usually expect the best”) and three items assessing trait pessimism (e.g., “I rarely count on good

things happening to me”; see Supplementary Materials C for the full scale). Participants

indicated the extent to which each statement characterized them on a 7-point scale (1: Strongly

Similar to Study 1B, a robustness check revealed that the results persisted (in both pattern and significance) when analyses included only the majority of participants who believed that Lakisha and Tamika were African American.


Disagree; 7: Strongly Agree). Responses were averaged to create a composite dispositional

optimism score (α = .84) and a dispositional pessimism score (α = .87).

We also assessed participants’ dispositional promotion and prevention focus by

administering the 18-item General Regulatory Focus Measure (GRFM; Lockwood, Jordan, &

Kunda, 2002). The GRFM contains nine items assessing promotion focus (e.g., “I frequently

imagine how I will achieve my hopes and aspirations”) and nine items assessing prevention

focus (e.g., “In general, I am focused on preventing negative events in my life”; see

Supplementary Materials C for the full scale). Participants indicated the extent to which each

statement characterized them on a 7-point scale (1: Not at all true of me; 7: Very true of me).

Responses were averaged to create a composite dispositional promotion focus score (α = .90) and

a dispositional prevention focus score (α = .83).


As in Study 1B, a Levene’s test revealed that the information-seeking index violated the

ANOVA’s homogeneity of variance assumption, F(2, 1197) = 3.45, p = .032. Therefore, as in

Study 1B, we employed a binomial logistic regression to analyze the information-seeking

index.11 This analysis revealed that there were no significant interactions between condition and

order on information seeking, Bs £ .52, SEs £ .44, zs £ 1.30, ps ³ .193; thus, we again collapsed

across order to analyze the effect of condition on information seeking.

11 A sensitivity power analysis with a data simulation approach indicated that the sample size employed in Study 1C provided 80% power to detect an effect (with a binomial logistic regression) for the difference between the planned contrasts of Cohen’s d = .20 and 60% power to detect an effect of Cohen’s d = .15. This sensitivity analysis was computed via a simulation procedure which conducted 1,000 simulations of three conditions (in which each condition had a sample size of 400) sampled from randomly-generated proportion data, and computed the minimum effect size that could be detected (with a binomial logistic regression) for the paired comparisons with this sample size.


A binomial logistic regression of condition on the information-seeking data revealed that

participants in the Intended Outcome condition (M = .69, SE = .02) sought a greater proportion of

positive information than participants in the Unintended Outcome condition (M = .52, SE = .02;

B = .72, SE = .15, z = 4.71, p < .001; Cohen’s d = .442, 95% CI [.296, .588]). Supporting our

theory, participants in the Natural condition (M = .64, SE = .02) also sought a greater proportion

of positive information than participants in the Unintended Outcome condition (B = .50, SE =

.14, z = 3.52, p < .001; Cohen’s d = .292, 95% CI [.155, .429]), and a similar proportion of

positive information as participants in the Intended Outcome condition (B = .22, SE = .15, z =

1.50, p = .135; Cohen’s d = .078, 95% CI [-.060, .215]).

We next examined whether dispositional empathy, optimism, pessimism, or regulatory

focus affected participants’ information-seeking behavior. To that end, we conducted separate

binomial logistic regression analyses of information seeking as a function of each dispositional

scale, the dummy-coded condition variable, and their interaction. The analyses revealed no

interaction between each trait index and the Natural (vs. Unintended Outcome) conditions

(Empathy: B = -.10, SE = .24, z = -.42, p = .674; Optimism: B = .03, SE = .15, z = .20, p = .839;

Pessimism: B = -.08, SE = .13, z = -.61, p = .541; Promotion Focus: B = -.12, SE = .15, z = -.78, p

= .435; Prevention Focus: B = -.08, SE = .16, z = -.49, p = .622), and no interaction between each

trait index and the Natural (vs. Intended Outcome) conditions (Empathy: B = .22, SE = .26, z =

.86, p = .388; Optimism: B = .25, SE = .16, z = 1.58, p = .114; Pessimism: B = -.08, SE = .14, z =

-.56, p = .574; Promotion Focus: B = .09, SE = .16, z = .55, p = .580; Prevention Focus: B = .19,

SE = .17, z = 1.15, p = .250).

Together, Studies 1A–1C provide converging evidence that when people predict an

individual’s competitive outcome, they naturally test the hypothesis that the individual will win.


This general bias is not constrained to contexts in which the individual whose outcome is

forecasted belongs to one particular gender, race, or age group, and it persists regardless of

whether people encounter information about this individual before or after learning about other

opponents.

Furthermore, Study 1C found that individual differences in dispositional empathy,

optimism, pessimism, and regulatory focus did not affect how participants in the Natural (vs.

Unintended Outcome or Intended Outcome) condition sought information, suggesting that these

variables are unlikely driving the selective hypothesis testing that people naturally engage in

when making other-oriented predictions.

Importantly, we predict that this selective hypothesis testing biases people’s ultimate

forecasts and leads them to overestimate a contestant’s likelihood of winning (H2). We examined

this prediction in Study 2A and Study 2B.

Study 2A: The Consequence of Selective Hypothesis Testing

We theorize that observers’ natural tendency to selectively test the hypothesis that

another individual will win biases their forecasts. Study 2A tested this proposition by

administering the same measure of selective hypothesis testing as in Studies 1B–1C (i.e.,

information seeking; Bassok & Trope, 1984; Nickerson, 1998; Snyder & Campbell, 1980) before

capturing participants’ forecasts. Of note, participants did not view the information that they

selected prior to formulating their forecasts; thus, while the information-seeking measure

illuminated the hypothesis that participants tested, it did not contaminate their actual forecasts.


Method

Three hundred one participants (mean age = 36 years; 48% male) from Mechanical Turk

participated in an online study in exchange for 30 cents.12 All participants read about two

photographers who entered a photography competition (as in Studies 1B–1C). Participants read

that the photographers were named Stan Richard and Mark Johnson, and viewed the photographs

that Stan and Mark submitted (Supplementary Materials B). As in the previous studies,

participants were randomly assigned to either the Natural, the Intended Outcome, or the

Unintended Outcome condition. Next, participants read that each participant in the survey had

been randomly assigned to think about what the outcome of the competition would be for either

Stan or Mark, and that they had been randomly assigned to think about what the outcome of the

competition would be for Stan. All participants then read that they could view information to

help them make their prediction. Participants then completed the same information-seeking

measure described in Studies 1B–1C.

After participants selected the information they wished to view, they read on the next

survey screen that they could read the selected information at the end of the survey. Before

participants viewed their selected information, they indicated their prediction of the

competition’s outcome by selecting a radio button labeled either “Stan will win” or “Stan will

lose.”

12 Because Studies 2A–5 examine the dependent measure of participants’ forecasts, we conducted an additional sensitivity power analysis to identify the effect size that these studies are powered to detect with the forecasting dependent variable (i.e., a binary variable). This sensitivity power analysis was computed via a simulation procedure which conducted 1,000 simulations of three conditions (in which each condition had a sample size of 100) sampled from randomly-generated binary data, and computed the minimum effect size that could be detected (with a chi square analysis) for the paired comparisons at this sample size. This analysis revealed that a sample of 300 participants (i.e., the target sample size in Studies 2A–5) would provide 80% power to detect an effect (with a chi square analysis) for the difference between the planned contrasts of Cohen’s d = .27 and 60% power to detect an effect of Cohen’s d = .23.



Information seeking. We first calculated the information-seeking index for each

participant via the procedures described in Studies 1B–1C. As in Studies 1B–1C, the Levene’s

test revealed that the information-seeking index violated the ANOVA’s homogeneity of variance

assumption, F(2, 297) = 9.27, p < .001. Therefore, we again employed a binomial logistic

regression to analyze the information-seeking index.

Conceptually replicating the previous studies, this regression revealed that participants in

the Intended Outcome condition (M = .69, SE = .03) sought a greater proportion of positive

information than did participants in the Unintended Outcome condition (M = .52, SE = .04; B =

.72, SE = .30, z = 2.39, p = .017; Cohen’s d = .514, 95% CI [.225, .804]), participants in the

Natural condition (M = .66, SE = .03) sought a greater proportion of positive information than

did participants in the Unintended Outcome condition (B = .60, SE = .30, z = 2.04, p = .042;

Cohen’s d = .409, 95% CI [.121, .696]), and participants in the Natural condition and Intended

Outcome condition sought a similar proportion of positive information (B = .11, SE = .30, z =

.37, p = .710; Cohen’s d = .081, 95% CI [−.194, .356]).

Forecasts. An omnibus chi-square analysis revealed that condition also significantly

affected participants’ forecasts of the competition’s outcome, χ2(df = 2, N = 301) = 16.87, p <

.001. Specifically, participants in the Intended Outcome condition predicted that Stan was more

likely to win (83.50%) than did participants in the Unintended Outcome condition (59.30%),

χ2(df = 1, N = 194) = 14.03, p < .001 (Cohen’s d = .557, 95% CI [.262, .852]). Participants in the

Natural condition also predicted that Stan was more likely to win (79.40%) than did participants

in the Unintended Outcome condition, χ2(df = 1, N = 198) = 9.50, p = .002 (Cohen’s d = .448,

95% CI [.160, .733]). Further supporting our theorizing, participants in the Natural condition and


the Intended Outcome condition predicted that Stan was similarly likely to win, χ2(df = 1, N =

210) = .57, p = .450 (Cohen’s d = .104, 95% CI [−.169, .377]).

In sum, these results provide initial evidence consistent with our theorizing that people

naturally overestimate another individual’s likelihood of winning when forecasting that

individual’s competitive outcome (H2): Although a pretest revealed that the submitted

photographs were equally likable (see footnote 8), when observers forecasted the outcome that

the photographer of one of these photographs would experience, observers systematically

forecasted that the (equally liked) photograph was now more likely to win. Of note, explicitly

instructing participants to consider why Stan’s intended outcome may not occur (i.e., in the

Unintended Condition) did not fully eliminate observers’ baseline tendency to predict that Stan

would win, which is consistent with research indicating that when the hypothesis people

naturally test directly opposes the hypothesis that an explicit instruction directs them to test, the

explicit instruction attenuates (but often does not fully eliminate) the impact of the hypothesis

that people naturally test on their forecasts (Weber et al., 2007).

From selective hypothesis testing to forecasts. To test the mediating role of information

seeking (i.e., a proxy of the hypothesis that participants selectively tested; e.g., Bassok & Trope,

1984; Nickerson, 1998; Snyder & Campbell, 1980) in determining the effect of condition on

participants’ forecasts, we conducted a mediation analysis with orthogonal contrast coding

(Rosenthal & Rosnow, 1985) and bootstrapping (Hayes, 2013). To that end, we used the

‘mediation’ package in the R programming language (which enables mediation analyses in

contexts in which the dependent variable is binary and the mediator violates the ANOVA’s

homogeneity of variance assumption; Tingley, Yamamoto, Hirose, Keele, & Imai, 2017). The

first contrast compared the Unintended Outcome condition to the two other conditions. The


second contrast compared the Intended Outcome condition to the Natural condition. As

hypothesized, a bias-corrected mediation analysis with 5,000 nonparametric bootstraps revealed

that information seeking mediated the effect of the first contrast (controlling for the second

contrast) on forecasts (95% CI [.001, .104]; see Figure 1).

In sum, Study 2A provides additional evidence that people spontaneously test the

hypothesis that another individual will win when they forecast an individual’s competitive

outcome (H1). Study 2A further suggests that this selective hypothesis testing biased people to

overestimate the likelihood that Stan would win the competition (H2). Of note, mediation

analyses provide suggestive but not definitive evidence of a phenomenon’s underlying causal

process (e.g., Spencer, Zanna, & Fong, 2005); thus, after further testing the generalizability of

this mediation pattern in Study 2B, we then employed a process-by-moderation design to more

directly capture the causal relationship between hypothesis testing (guided by the proposed

intent-to-outcome lay belief) and forecasts (H2 and H3) in Study 3.

Study 2B: Generalizability of the Forecasting Bias

Study 2B examined the robustness and generalizability of the forecasting bias

documented in Study 2A. To that end, and in contrast to Study 2A, Study 2B investigated

people’s forecasts of the competitive outcome that will be experienced by a member of a social

category that is not stereotypically perceived as highly competent and agentic—a disabled

individual (Cuddy et al., 2008). To further examine generalizability, the individual was also

female (in contrast to the male contestant in Study 2A). Since we theorize that people

spontaneously test the hypothesis that an individual will achieve their intention because of a


general intent-to-outcome lay belief (rather than because of any group-specific stereotypes), we

predict that the forecasting bias documented in Study 2A will continue to emerge in Study 2B’s

forecasting context.

Method

Three hundred eight participants (mean age = 36 years; 33% male) from Mechanical

Turk participated in an online study in exchange for 30 cents. The procedures were identical to

those in Study 2A, with two exceptions: All participants in Study 2B read that the photographers’

names were Sara Jones and Anne Smith (rather than Stan and Mark), and that Sara and Anne

were both born paraplegic (i.e., they were both born without the use of their legs) and therefore

use wheelchairs.13 In addition, the order in which participants read the women’s names (i.e.,

“Sara and Anne” or “Anne and Sara”) was again counterbalanced, as was the name of the

particular individual whose outcome participants learned that they had been randomly assigned

to forecast.


Information seeking. We first calculated the information-seeking index for each

participant via the procedures described in Studies 1B–2A. The Levene’s test revealed that the

information-seeking index satisfied the ANOVA’s homogeneity of variance assumption, F(2,

305) = 2.17, p = .116. Therefore, we implemented our a priori analysis plan to employ an

13 In order to verify that this manipulation successfully led participants to perceive the women as disabled, we captured participants’ perceptions of whether each woman was disabled at the end of the survey. The majority of participants indicated that both Anne (87.0%; χ2(df = 1, N = 308) = 168.78, p < .001; Cohen’s d = 2.199, 95% CI [1.865, 2.533]) and Sara (86.0%; χ2(df = 1, N = 308) = 160.01, p < .001; Cohen’s d = 2.076, 95% CI [1.752, 2.400]) were disabled. A robustness check revealed that Study 2B’s results persisted (in both pattern and significance) when analyses included only the majority of participants who believed that Anne and Sara were disabled.


ANOVA to examine the information-seeking index. This ANOVA revealed that there were no

significant interactions between condition and order on information seeking, Fs(1, 296) £ .79, ps

³ .429; thus, we collapsed across order to analyze the effect of condition on information seeking.

An ANOVA of condition on the information-seeking data revealed that condition

significantly affected participants’ information seeking, F(2, 305) = 6.51, p = .002. Replicating

prior studies, planned contrasts revealed that participants in the Intended Outcome condition (M

= .73, SE = .03) sought a greater proportion of positive information than did participants in the

Unintended Outcome condition (M = .60, SE = .03; Fisher’s LSD: p = .003; Cohen’s d = .415,

95% CI [.130, .700]), participants in the Natural condition (M = .74, SE = .03) sought a greater

proportion of positive information than did participants in the Unintended Outcome condition

(Fisher’s LSD: p = .001; Cohen’s d = .439, 95% CI [.162, .716]), and participants in the Natural

condition and Intended Outcome condition sought a similar proportion of positive information

(Fisher’s LSD: p = .802; Cohen’s d = .037, 95% CI [−.238, .313]).

Forecasts. Consistent with the fact that there were no interactions between order and

condition on information seeking, a binary logistic regression revealed that there were also no

interactions between condition and order on forecasts (bs £ .99, SEs ³ .80, ps ³ .252); thus, we

collapsed across order to explore the effect of condition on forecasts. An omnibus chi-square

analysis revealed that condition again significantly affected participants’ forecasts of the

competition’s outcome, χ2(df = 2, N = 308) = 31.57, p < .001. Specifically, participants in the

Intended Outcome condition predicted that the photographer whose outcome they forecasted was

more likely to win (81.40%) than did participants in the Unintended Outcome condition

(46.00%), χ2(df = 1, N = 197) = 26.67, p < .001 (Cohen’s d = .789, 95% CI [.486, 1.092]).

Participants in the Natural condition also predicted that the photographer whose outcome they


forecasted was more likely to win (73.90%) than did participants in the Unintended Outcome

condition, χ2(df = 1, N = 206) = 17.13, p < .001 (Cohen’s d = .593, 95% CI [.309, .877]). Further

supporting our theorizing, participants in the Natural condition and the Intended Outcome

condition predicted that the photographer whose outcome they forecasted was similarly likely to

win, χ2(df = 1, N = 208) = 1.70, p = .193 (Cohen’s d = .181, 95% CI [−.094, .456]).

From selective hypothesis testing to forecasts. We employed the same procedures

described in Study 2A to test the mediating role of information seeking (i.e., a proxy of the

hypothesis that participants selectively tested; Bassok & Trope, 1984; Nickerson, 1998; Snyder

& Campbell, 1980) in determining the effect of condition on participants’ forecasts. As

hypothesized, and replicating Study 2A, information seeking mediated the effect of the first

contrast (controlling for the second contrast) on forecasts (95% CI [.0048, .0259]; see Figure 1).

In sum, Study 2B conceptually replicated Study 2A’s findings in a context in which

people forecasted the outcome that would be experienced by a member of a different social

group—a disabled female. To further investigate the bias’s robustness to the particular group

membership of the individual whose outcome people forecast, we conceptually replicated

Studies 2A–2B in a third replication (Supplementary Materials D; see Table 1 for a summary of

the supplemental studies) in which the contestant belonged to yet another social category—an

African American female.

Also of note, in an additional replication study (Supplementary Materials E), we

counterbalanced the order in which we captured participants’ forecasts and spontaneous

hypothesis testing. As expected, we did not detect order effects—regardless of measurement

order, participants naturally overestimated the likelihood that the individual whose outcome they

forecasted would win, and participants’ spontaneous hypothesis testing mediated this bias (H2).


Along with Studies 1A–1C, these replication studies provide converging evidence that the

observed phenomenon constitutes a general bias which is robust to order effects, and which

pervades forecasts of the outcome that a diverse range of others will experience.

Study 3: The Underlying Role of the Intent-to-Outcome Lay Belief

We propose that a lay belief—that others generally achieve their intentions—underlies

the selective hypothesis testing and the resulting biased forecasts observed in Studies 1–2 (H3).

In Study 3, we employ a moderation design to test this prediction (e.g., Spencer, Zanna, & Fong,

2005). Specifically, we draw from prior literature on schemas and exemplars to directly

manipulate this lay belief. This literature has shown that exposure to schemas, examples, and

exemplars inconsistent with people’s lay beliefs temporarily weaken those beliefs (Blair, Ma, &

Lenton, 2001; Dasgupta & Asgari, 2004; Dasgupta & Greenwald, 2001). For example, people

have less automatic racial bias following incidental exposure to admired African Americans

(Dasgupta & Greenwald, 2001). Drawing on this research, if the intent-to-outcome lay belief

drives the biased forecasts documented thus far, then incidental exposure to examples

inconsistent with this lay belief (e.g., examples of others’ failures to achieve their intentions)

should attenuate this bias by temporarily weakening this lay belief. We test this possibility in

Study 3.

Also important, Study 3 directly measures the proposed lay belief. We predict that people

naturally believe that others generally achieve their intended outcomes. Drawing on prior

literature (e.g., Dasgupta & Greenwald, 2001), we further theorize that incidental exposure to

others’ failures to achieve their intentions attenuates people’s belief that others generally achieve


their intended outcomes, and attenuates the previously-documented forecasting bias as a result.

Study 3 also further examines the generalizability of the proposed phenomenon by testing it in a

different domain—a sports competition.

Method


participated in this study in exchange for 35 cents. All participants read about a golfer named

Jayden Darcy who would soon be competing in a golf match against another golfer named Brian

Suke. Participants viewed pictures of both Jayden Darcy and Brian Suke (see Supplementary

Materials F),14 and were asked what they thought the outcome of the competition would be for

Jayden. Participants indicated their prediction of the competition’s outcome by selecting a radio

button labeled either “Jayden will win” or “Jayden will lose.” After participants entered their

prediction, on the next survey page participants indicated the extent to which they endorsed the

intent-to-outcome lay belief. To that end, they indicated how often people achieve the outcomes

they intend. Participants indicated their responses on a 7-point scale (1: Not often at all; 7: Very

often).

Importantly, prior to reading about the upcoming golf match between Jayden Darcy and

Brian Suke, participants were randomly assigned to one of three conditions at the beginning of

the study—a Lay Belief Consistent condition, a Lay Belief Attenuated condition, or a Natural Lay

Belief condition. In order to attenuate the intent-to-outcome lay belief, we presented participants

14 In a pretest, 200 participants drawn from the same pool (mean age = 36; 46% male) viewed the same two pictures and were randomly assigned to answer questions about either Jayden or Brian. Participants assigned to answer questions about Jayden (vs. Brian) indicated the extent to which they thought that Jayden (vs. Brian) was a good golf player. Participants indicated their responses on a 7-point scale (1: Not at all; 7: Very much). There was no difference in perceptions of how good a golfer Jayden (M = 4.65, SD = 1.00) or Brian (M = 4.44, SD = 1.18) was, t(198) = 1.35, p = .179 (Cohen’s d = .191, 95% CI [−.089, .471]).


in the Lay Belief Attenuated condition with sample events inconsistent with this lay belief (via a

procedure adapted from prior research; e.g., Blair, Ma, & Lenton, 2001; Dasgupta & Asgari,

2004; Dasgupta & Greenwald, 2001). Specifically, participants in the Lay Belief Attenuated

condition viewed information about three individuals who had previously competed in three

different athletic competitions and lost. In order to control for any consequence of simply

viewing information about other unrelated competitors on subsequent forecasts, participants in

the Lay Belief Consistent condition read about these same three individuals competing in the

same three athletic competitions, but these participants read instead that these individuals won

(i.e., participants in the Lay Belief Consistent condition viewed information consistent with the

proposed intent-to-outcome lay belief). Participants in both the Lay Belief Attenuated condition

and the Lay Belief Consistent condition then completed a filler task (in which they rated on

separate 7-point scales the extent to which the information about the three competitors was

interesting and enjoyable to read) before viewing the focal information about Jayden. In contrast,

participants in the Natural Lay Belief condition did not view any information prior to viewing the

focal information about Jayden and Brian.


Lay belief manipulation check. An ANOVA of condition on the lay belief data revealed

that condition significantly affected participants’ lay beliefs, F(2, 297) = 13.39, p < .001. As

expected, planned contrasts revealed that participants in the Lay Belief Consistent condition (M =

4.29, SE = .14) expressed greater belief that others achieve their intended outcomes than did

participants in the Lay Belief Attenuated condition (M = 3.59, SE = .13; Fisher’s LSD: p < .001;

Cohen’s d = .523, 95% CI [.234, .812]). Importantly, participants in the Natural condition (M =


4.48, SE = .11) also expressed greater belief that others achieve their intended outcomes than did

participants in the Lay Belief Attenuated condition (Fisher’s LSD: p < .001; Cohen’s d = .725,

95% CI [.439, 1.011]), and participants in the Natural condition expressed the same degree of

belief that others achieve their intended outcomes as did participants in the Lay Belief Consistent

condition (Fisher’s LSD: p = .290; Cohen’s d = .150, 95% CI [−.127, .427]). In sum, these

analyses indicate that Study 3’s lay belief manipulation successfully manipulated participants’

beliefs.

Also important, further analysis of the lay belief data revealed that participants in the

Natural Lay Belief condition and the Lay Belief Consistent condition on average believed that

others achieve their intentions: A one-sample t-test revealed that participants’ responses were

significantly above the scale’s mid-point of four in both conditions (t(96) = 4.31, p < .001,

Cohen’s d = .420, 95% CI [.221, .616]; t(106) = 2.09, p =.039, Cohen’s d = .213, 95% CI [.011,

.414]; respectively). By contrast, participants in the Lay Belief Attenuated condition on average

did not believe that others achieve their intentions: A one-sample t-test revealed that participants’

responses were significantly below the scale’s mid-point of four, t(95) = 3.08, p = .003 (Cohen’s

d = .317, 95% CI [.112, .520]). Thus, participants naturally believed that others achieve their

intended outcomes (i.e., in the Natural Lay Belief condition), but exposure to multiple

counterexamples (i.e., in the Lay Belief Attenuated condition) attenuated this lay belief.

Forecasting. An omnibus chi-square analysis revealed that this lay belief manipulation

significantly affected participants’ forecasts of the golf match’s outcome, χ2(df = 2, N = 300) =

44.33, p < .001. Specifically, participants in the Lay Belief Consistent condition (84.50%)

predicted that Jayden was more likely to win than did participants in the Lay Belief Attenuated

condition (43.80%), χ2(df = 1, N = 193) = 34.94, p < .001 (Cohen’s d = .938, 95% CI [.623,


1.253]). Consistent with our theorizing, participants in the Natural Lay Belief condition (78.50%)

also predicted that Jayden was more likely to win than did participants in the Lay Belief

Attenuated condition, χ2(df = 1, N = 203) = 25.96, p < .001 (Cohen’s d = .764, 95% CI [.467,

1.061]), and participants in the Natural Lay Belief condition and the Lay Belief Consistent

condition predicted that Jayden was similarly likely to win, χ2(df = 1, N = 204) = 1.22, p = .269

(Cohen’s d = .155, 95% CI [−.123, .432]).

In sum, Study 3 provides evidence that an intent-to-outcome lay belief underlies the

documented bias (H3): Participants whose natural lay beliefs were not disturbed and those who

were exposed to lay-belief-consistent outcomes similarly believed that others achieve their

intended outcomes, and these participants similarly overestimated the likelihood that an

individual would achieve his intended outcome. Exposure to lay-belief-inconsistent information

(which weakens this lay belief; see Blair et al., 2001; Dasgupta & Asgari, 2004; Dasgupta &

Greenwald, 2001) reduced the intent-to-outcome lay belief and attenuated this forecasting bias.

Study 3’s moderation-of-process design (Spencer et al., 2005) thus provides evidence for the

driving role of the intent-to-outcome lay belief in shaping people’s forecasts for others.

Study 4: When a Competition Singles Out the Worst Competitor

So far we have tested our theory by examining competitions designed to identify and

recognize the single best competitor. We examined our theorizing in such contexts because of

their ubiquity and immediate relevance to everyday decision making. However, perhaps the

contexts examined in the previous studies leave the previous findings open to an alternative

explanation—in particular, it could be argued that the observed bias is due to focalism (Wilson,


Wheatley, Meyers, Gilbert, & Axsom, 2000), whereby participants in Studies 1–3 selectively

tested the hypothesis that an individual would experience a winning outcome simply because the

competitions in these studies were designed to identify a single winner, and this design drew

participants’ focus to the individuated winning outcome rather than to the alternative (e.g., all the

undifferentiated non-winners). Under this alternative account of focalism, switching a

competition’s goal from identifying the best entrant to identifying the worst entrant should

reverse the bias (i.e., participants should naturally overestimate the likelihood that a focal actor

will be the single worst entrant (i.e., lose) in the competition).

We investigate this possibility in Study 4. To this end, we examine people’s predictions

about a competition that was launched exclusively to identify a single worst entrant, rather than

to identify a single best entrant. If the bias documented in the prior studies is indeed driven by

the intent-to-outcome lay belief rather than the alternative account of focalism, participants

should still overestimate the likelihood that a focal entrant will win (i.e., not lose) in this context,

despite the fact that losing is the focal outcome that the competition is designed to identify and

spotlight.

Method


participated in an online study in exchange for 30 cents. All participants read about four

photographers who are colleagues and go to an annual dinner together to celebrate the year.

Participants further read that one of the photographers pays for everyone’s dinner each year. The

way that the photographers determine who must pay for the dinner is with a photo

competition: Before the dinner, all the photographers submit a photo that they have taken to an


independent third party who judges the photos based on their quality. The photographer whose

photo is selected by the third party as the lowest-quality photo pays for the dinner, which is an

outcome that all the photographers aim to avoid.

Next, participants learned that they would predict the outcome of the competition for one

of the photographers who was named Stan. Participants then viewed the four submitted

photographs (which a pretest revealed were perceived of equivalent quality),15 and were

randomly assigned to the Natural condition, the Intended Outcome condition, or the Unintended

Outcome condition (adapted from the previous studies). Finally, all participants indicated their

prediction of the competition’s outcome by selecting either a radio button labeled “Stan’s photo

will be judged as being of the lowest quality” or a radio button labeled “Stan’s photo will not be

judged as being of the lowest quality.”


An omnibus chi-square analysis revealed that condition significantly affected

participants’ forecasts, χ2(df = 2, N = 300) = 28.18, p < .001. Specifically, participants in the

Natural condition (22.10%) less frequently predicted that Stan would lose than did participants

in the Unintended Outcome condition (48.5%), χ2(df = 1, N = 192) = 14.57, p < .001 (Cohen’s d

= .572, 95% CI [.275, .869]). Participants in the Intended Outcome condition (16.17%) also less

frequently predicted that Stan would lose than did participants in the Unintended Outcome

condition, χ2(df = 1, N = 205) = 23.85, p < .001 (Cohen’s d = .724, 95% CI [.430, 1.018]).

15 In a pretest, 301 participants drawn from the same pool (mean age = 34; 47% male) were randomly assigned to assess the quality of one of the photographs. Specifically, participants viewed the photographs and were randomly assigned to indicate the extent to which one of the photographs was of low quality. Participants indicated their responses on a 7-point scale (1: Not at all; 7: Very much). An omnibus ANOVA revealed that there was no difference in perceptions of the four photographs’ quality, F(3, 297) = 1.22, p = .304.


Participants in the Natural condition and the Intended Outcome condition predicted with similar

frequency that Stan would lose, χ2(df = 1, N = 203) = .96, p = .326 (Cohen’s d = .138, 95% CI

[−.140, .416]).

In sum, Study 4 showed that the proposed effect persists when a competition is designed

to single out a worst entrant rather than to single out a best entrant. Replicating the previous

studies, even when losing was the most individuated (and thus potentially the most salient)

outcome, participants again naturally forecasted that an individual would achieve his intended

outcome of winning with a similar frequency as those who were explicitly instructed to consider

this hypothesis. Thus, the current bias is unlikely to be driven by a tendency to simply

overestimate the likelihood that a focal actor will achieve the most individuated (and thus

potentially the most salient) outcome. Together, Studies 3 and 4 provide converging evidence for

our theorizing that an intent-to-outcome lay belief, rather than focalism, leads forecasters to

overestimate the likelihood that a contestant in a competition will win.

Study 5: Incentivizing Accuracy

People desire to accurately predict the future (Peetz & Buehler, 2009; Pelham & Neter,

1995). People engage in selective hypothesis testing despite this goal of making accurate

forecasts because their limited cognitive resources require them to evaluate one hypothesis at a

time, and this sequential process of hypothesis testing results in the first evaluated hypothesis

biasing their ultimate judgment (Epley & Gilovich, 2005; Wilson et al., 1996). Drawing on this

literature, we propose that the bias documented in the current research occurs despite observers’

goal of making accurate forecasts. To directly test this proposition, in Study 5 we incentivized


participants to make accurate forecasts by providing them additional compensation if their

forecast was indeed accurate.

Study 5 also had an additional goal. Specifically, it conducted a more conservative test of

our hypothesis that observers apply the intent-to-outcome lay belief even in contexts in which

this lay belief is blatantly and clearly false—when the objective base rate of success is low. To

this end, Study 5 examines a competition context in which the base rate probability of winning is

25%.

Method

Three hundred two participants (mean age = 36 years; 39% male) from Mechanical Turk

participated in a study in exchange for 30 cents. As in Study 1A, all participants read about four

professional graphic designers, named Stan Richard, Mark John, Craig Darcy, and Tim Barry,

who designed and submitted a logo for a new café. Participants learned that the café had decided

that MTurkers would vote for the logo they liked best, that the café would use the winning logo,

and that the café would pay the winning designer a large cash prize. All participants read that

they had been randomly assigned to think about what the outcome would be for one of the

designers, and that they had been randomly assigned to think about the outcome for Tim Barry.

Participants were directly incentivized to be accurate in their predictions: They were instructed

that if they correctly forecasted the outcome for Tim, they would receive a 20-cent bonus.

Participants then viewed each of the logos that the four designers submitted (Supplementary

Materials A), which a pretest revealed were equally liked (see Study 1A).


Participants were then randomly assigned to one of three conditions—a Natural

condition, an Intended Outcome condition, or an Unintended Outcome condition. These

conditions were manipulated as in Study 1A.

Next, all participants indicated their prediction of the competition’s outcome for Tim by

selecting a radio button labeled with either “Tim will win” or “Tim will lose.”


An omnibus chi-square analysis revealed that condition significantly affected

participants’ forecasts of Tim’s outcome in the competition, χ2(df = 2, N = 302) = 11.78, p =

.003. Specifically, participants in the Intended Outcome condition predicted that Tim was more

likely to win (85.30%) than did participants in the Unintended Outcome condition (65.90%),

χ2(df = 1, N = 193) = 9.93, p = .002 (Cohen’s d = .465, 95% CI [.172, .757]). Participants in the

Natural condition also predicted that Tim was more likely to win (81.70%) than did participants

in the Unintended Outcome condition, χ2(df = 1, N = 200) = 6.45, p = .011 (Cohen’s d = .364,

95% CI [.080, .648]). Further consistent with our theory and the findings in the prior studies,

participants in the Natural condition and the Intended Outcome condition made similar

predictions about Tim’s outcome in the competition, χ2(df = 1, N = 211) = .51, p = .477 (Cohen’s

d = .098, 95% CI [−.175, .370]).

Together, these results further enhance the robustness and generalizability of the findings

documented in the prior studies. Even when people were directly incentivized to be accurate in a

context with a base rate likelihood of success as low as 25%, people naturally forecasted that an

individual would achieve his intended outcome of winning with a similar frequency as those who

were explicitly instructed to consider this hypothesis; moreover, these participants were more


likely to predict that this contestant would win than were participants who were explicitly

instructed to test the hypothesis that this person would not achieve his intended outcome (i.e.,

would lose). These findings underscore the persistence of the documented bias when people

make other-oriented forecasts.

General Discussion

Across eight studies examining people’s predictions of the competitive outcomes that

will be experienced by a diverse range of individuals (individuals with different genders,

ethnicities, ages, and disability statuses) in a variety of competitive contexts (logo competitions,

photography competitions, and golf tournaments), we find that when observers forecast the

competitive outcome that another individual will experience, they spontaneously and selectively

test the hypothesis that the individual will win (H1; Studies 1–2). This selective hypothesis

testing biases people’s forecasts, leading them to overestimate the likelihood that the individual

will win (H2; Studies 2–5). Following the procedures outlined by McShane & Böckenholt

(2017), we conducted a single-paper meta-analysis of all confirmatory (but not exploratory)

studies that we ever conducted which included the three focal conditions (i.e., the Intended

Outcome condition, the Unintended Outcome condition, and the Natural condition) and the

forecasting dependent variable. This meta-analysis estimated that 54.8% of participants in

the Unintended Outcome condition forecasted that a contestant would win a competition,

compared to 76.03% in the Intended Outcome condition (Cohen’s d = .476, 95% CI [.409, .543])

and compared to 72.46% in the Natural condition (Cohen’s d = .365, 95% CI [.300, .430]);

consistent with our theory, the forecasts that people generated naturally (i.e., in the Natural

condition) were significantly more similar to the forecasts generated by people who were


explicitly instructed to test the hypothesis that a competitor would win (i.e., in the Intended

Outcome condition) than to the forecasts generated by people who were explicitly instructed to

test the hypothesis that a competitor would lose (i.e., in the Unintended Outcome condition; 95%

CI of the difference between the two contrasts = [.182, .243]; see Supplemental Materials H for

further detail about the single-paper meta-analysis results).

Process-by-moderation evidence further indicates that an intent-to-outcome lay belief

underlies this bias (H3; Study 3), and people selectively test hypotheses consistent with this lay

belief. We further examine and rule out the alternative accounts of focalism (Study 4), primacy

(Studies 1B–1C, Study 2B, Supplementary Materials D–E, and Supplementary Materials G), and

group-specific stereotypes (Studies 1B–1C, Study 2B, and Supplementary Materials D). Last, we

find that this bias persists even when individuals are directly incentivized to generate accurate

forecasts in a context in which the base rate probability that a contestant will win is clearly low

(Study 5).

Theoretical Contribution

Our research contributes to several important streams of literature. First, it contributes to

the lay belief literature by illuminating a previously undocumented lay belief—that others

generally achieve their intentions. We further find that people misapply this lay belief to contexts

in which it is probabilistically inaccurate (e.g., competitions designed to allow only one of many

entrants to win). This research thus adds to the growing body of work on the overgeneralization

of lay theories to situations where they are objectively inaccurate (Haws, Reczek, & Sample,

2017; Tversky & Kahneman, 1973; Tversky, Kahneman, & Moser, 1990), by uncovering a novel

lay belief that can lead people’s forecasts astray.


Our research also builds upon and advances the forecasting literature: The current

research provides novel insight into the architecture of an important process underlying

forecasting—spontaneous hypothesis testing—and in doing so illuminates a unique systematic

bias in the hypotheses that people naturally test when forecasting the outcomes that others will

experience. Substantial prior research has uncovered the biases that govern people’s forecasts of

their own future behavior and the outcomes that they themselves will experience (e.g., Dunning

& Story, 1991; Epley & Dunning, 2000; Krizan & Windschitl, 2007; Weinstein, 1980).

However, people not only forecast the outcomes that they will experience, but also frequently

forecast the outcomes that others will experience (Hobson, 2015). We provide insight into how

people generate these forecasts and illuminate a previously undocumented lay belief that drives

these forecasts. In doing so, we also uncover two interventions that can reduce this bias—explicit

instructions (Studies 2A, 2B, 4, and 5) and weakening the lay belief that underlies it (Study 3).

Moreover, the current insights also contribute to the hypothesis testing literature. Prior

research in this domain has largely relied on explicit instructions to study the impact of

hypothesis testing on how observers forecast the outcomes that others will experience (e.g.,

Bassok & Trope, 1984; Gibson et al., 1997; Snyder, Campbell, & Preston, 1982). Our research

illuminates the hypotheses that observers naturally test, and reveals that observers naturally

generate and selectively test the hypothesis that others will achieve their intended outcomes,

which biases observers’ ultimate forecasts.

We focused our examination on competition contexts because of their prevalence in

everyday decision making (e.g., Hobson, 2015). At a broader level, our theoretical framework

may also provide insight into how observers make predictions about the outcomes that others

will experience in noncompetitive (yet still uncertain) contexts. For example, when people


forecast the likelihood that others will achieve personal goals (e.g., eating at least one vegetable a

day, volunteering at least once a month, reading the newspaper at least once a week), our

framework suggests that forecasters may spontaneously first test the hypothesis that others will

achieve these intentions, and thus may be biased to predict that they will. We encourage future

research to investigate this possibility.

Boundaries and Extensions

Possible sources of the intent-to-outcome lay belief. As previously noted, the intent-to-

outcome lay belief may originate from a rich array of sources, including people’s observation of

covariation, the belief that others’ ex post outcomes reflect their prior intent, and the

phenomenon of positivity offset. Popular aphorisms may also reinforce and propagate the intent-

to-outcome lay belief—for example, horse racing enthusiasts often note that “the rider that wins

the race is the rider that wanted it more than anyone else” (Allen, 2014). Similarly, people who

win a competition often attribute their victory to the fact that they “wanted to win more” than

their losing adversaries (Farrell, 2016; Griffiths, 2017; Hill, 2017; Sharland, 2015; Silver, 2018).

Because lay theories are often transmitted and perpetuated via public communications

(McFerran, 2015; Morris, Menon, & Ames, 2001), it is possible that such publicly used

attributions reflect and reinforce the proposed intent-to-outcome lay belief.

Another potential source of the intent-to-outcome lay belief relates to self-efficacy.

Specifically, prior research suggests that observers’ perceptions of similar (but not of dissimilar)

others’ outcomes can influence observers’ own self-efficacy (Bandura, 1977; Bandura, 1995;

Kazdin, 1974; Meichenbaum, 1977; Schunk, 1989). Is it possible that participants perceived the

individuals described in the prior studies as similar to themselves, and this perception motivated


participants to selectively test the hypothesis that these similar others would achieve their

intentions? The current research may provide initial insight into this possibility. Specifically,

four studies (Studies 1B, 1C, 2B, and the study described in Supplemental Materials D) provide

converging evidence that the current phenomenon persists when participants forecast the

outcomes of paraplegic, elderly, and African American individuals. Prior research indicates that

such racial, generational, and disability-related differences between the participants (the majority

of whom differed on these dimensions from the target individuals whose outcome they

forecasted; Huff & Tingley, 2015; Levay, Freese, & Druckman, 2016) and the target individuals

likely generated perceptions of interpersonal dissimilarity (Jones, Moore, Stanaland, & Wyatt,

1998; Whittler, 1991; Whittler & Spira, 2002; Zellmer-Bruhn, Maloney, Bhappu, & Salvador,

2008). Because people’s own self-efficacy is unlikely to be influenced by the outcomes of others

perceived as dissimilar to themselves (Bandura, 1977; Bandura, 1995; Kazdin, 1974;

Meichenbaum, 1977; Schunk, 1989), we suspected that a motivation to protect one’s own self-

efficacy is unlikely to underlie the current phenomenon.

Nevertheless, we directly investigated this possibility in an additional study in

Supplemental Materials G. Participants in this study read about an individual who they perceived

as dissimilar to themselves (as well as immoral and unlikeable)—an individual who worked at a

big tobacco company and who desired to produce ads targeted to encourage teenagers to smoke.

We found that participants perceived this individual as dissimilar to themselves, but continued to

selectively test the hypothesis that this individual would achieve his intended outcome in a

competition. This study thus demonstrates that the forecasting bias persists even when

forecasting the outcome that will be experienced by an individual who the forecasters perceive as


dissimilar to themselves (and thus whose success likely would not influence their own self-

efficacy; Bandura, 1977; Bandura, 1995; Kazdin, 1974; Meichenbaum, 1977; Schunk, 1989).

Alternatively, could just world beliefs about others (e.g., Lerner, 1980; Rubin & Peplau,

1975) contribute to the intent-to-outcome lay belief? Research investigating people’s just world

beliefs finds that people believe that good things should happen to good people (Lerner, 1980;

Rubin & Peplau, 1975); to the extent that positivity offset often leads people to feel mildly

favorable about unknown others, perhaps just world beliefs lead people to infer that unknown

others are generally able to achieve their intended outcomes. The previously-described

supplemental study in Supplemental Materials G may also provide initial insight into this

possibility. The pretest in this study revealed that participants perceived the individual in this

study as immoral (likely because this individual worked at a big tobacco company and desired to

encourage teens to smoke; see Supplemental Materials G); the just world alternative thus would

suggest that forecasters would not have applied the intent-to-outcome lay belief to this immoral

other (because the just world hypothesis suggests that observers believe that bad (rather than

good) outcomes happen to immoral people; Lerner, 1980; Rubin & Peplau, 1975). By contrast,

this study found that people continue to selectively test the hypothesis that an immoral individual

will achieve his intended outcome. This study thus provides initial evidence inconsistent with the

possibility that just world beliefs underlie the current bias. We encourage future research to

further explore this possibility, as well as other processes that may contribute to the intent-to-

outcome lay belief.

The target of the forecast. Future research can also profit from investigating the factors

that shape people’s decisions about which particular individual in a multiperson competition is

the target of the forecast in the first place. Although some competitive contexts naturally


highlight a single contestant (e.g., as occurs when the world’s attention is fixated on whether a

particular individual will break a world record), other competitive contexts involve numerous

salient contestants. Because people’s cognitive limitations often prevent them from testing more

than one hypothesis at a time (e.g., Epley & Gilovich, 2005; Wilson et al., 1996), simultaneously

testing the prediction that multiple contestants may win likely exceeds people’s cognitive limits.

Our studies examine the natural forecasting process that unfolds when people are prompted to

forecast the outcome for a particular contestant, mirroring the external prompts that pervade

people’s daily lives (e.g., a coworker’s query about the outcome that a particular contestant will

experience, a trailer for the Winter Olympics that spotlights a particular athlete). Future research

could examine other factors that may also influence the target that forecasters consider.

Possible moderators. As previously noted, and consistent with our theoretical framework,

a single paper meta-analysis found that the forecasts that people naturally generated were

significantly more similar to the forecasts generated in the Intended (vs. Unintended) Outcome

condition. In addition to quantifying the magnitude of the dominating influence of this theorized

bias on forecasts, the meta-analysis detected a small difference (i.e., a difference

of 3.57%) between people’s natural forecasts and the forecasts that they generate when they are

explicitly instructed to test the hypothesis that an individual will achieve their intended outcome.

Because even practically meaningless effects (i.e., contrasts with effect sizes so small that they

have no practical impact) can become detectable with enough statistical power, examining the

effect size of contrasts provides key insight into their practical validity and importance (Kim,

2015; Lantz, 2013; Levine, Weber, Hullett, Park, & Lindsey, 2008; Sullivan & Feinn, 2012).

Importantly, the effect size of this contrast (i.e., the difference between people’s natural forecasts

and the forecasts that people generate when they are explicitly instructed to test the hypothesis


that an individual will achieve their intended outcome) is indeed extremely small (i.e., an effect

size of Cohen’s d = .106, 95% CI [.043, .170], which is well below the threshold of a small

effect; Cohen, 1992). Further consistent with our theory, the contrast between the Natural (vs.

Unintended Outcome) condition (i.e., which produced a medium effect size; Cohen’s d = .365,

95% CI [.300, .430]) was significantly larger than the small contrast between the Natural (vs.

Intended Outcome) condition (95% CI of the difference between the two contrasts = [.182,

.243]); in other words, the forecasts that people naturally generated were significantly more

similar to the forecasts generated in the Intended (vs. Unintended) Outcome condition.

Nevertheless, we encourage future research to explore the origin of this small unpredicted

difference between the Natural and the Intended Outcome conditions that emerged in the meta-

analysis. We suspect that Study 3 may provide relevant insight: Study 3 reveals that people on

average endorse the intent-to-outcome belief, but that there are individual differences in the

extent of this endorsement (as is the case with many widely held lay beliefs; e.g., Chao, Chen,

Roisman, & Hong, 2007; Fisher, 2003; Furnham, 2003). Therefore, it is possible that the

minority of people who less strongly endorsed the intent-to-outcome lay belief contributed to a

detectable (yet small) difference when viewed under the lens of increasingly high levels of

statistical power. We encourage future research to investigate this possibility.

Also relevant to future research, it is important to note that there may be additional

constraints on the documented bias. We uncovered one in the current research: The bias

attenuates when people’s lay belief that others generally achieve their intentions is temporarily

weakened (e.g., when people are incidentally exposed to others’ failures to achieve their

intentions, as in Study 3). In a similar vein, the relevance of numerous lay beliefs to a particular

forecasting context may also attenuate the influence of the intent-to-outcome lay belief on the


hypothesis that people selectively test. Our studies document the dominating influence of the

intent-to-outcome lay belief on selective hypothesis testing across a diverse range of competition

contexts, including athletic competitions, artistic competitions, and marketing competitions.

Nevertheless, individual observers may have a lay belief that most people fail to obtain their

intentions in a particular domain. For example, imagine that a particular observer has a lay belief

that most diets fail. The hypothesis that this observer selectively tests when forecasting the

outcome that a contestant in a weight loss challenge will experience may be influenced not only

by the documented intent-to-outcome lay belief, but also by this additional dieting lay belief. In

such contexts, it is possible that the relative strength and salience of additional lay beliefs would

affect the degree to which the intent-to-outcome lay belief shapes the observer’s forecast. Future

research could examine the potential dynamics that may emerge from the interplay of multiple

lay beliefs.

In a similar vein, the behavior prediction literature suggests that explicit information

about a competition’s difficulty may also moderate the impact of the intent-to-outcome lay belief

on the hypothesis that forecasters selectively test. Specifically, this research reveals that

information about a particular competition’s difficulty—as well as information about other

situational variables that may facilitate or impede an individual’s likelihood of success—

influences forecasts of the contestant’s outcome (Collins & Barber, 2005; Feltz, & Lirgg, 2001;

Slobounov, Yukelson, & O’brien, 1997; Van-Yperen & Duda, 1999). Although our studies

document the dominating influence of the intent-to-outcome lay belief on selective hypothesis

testing across a diverse range of competition contexts (e.g., athletic competitions, marketing

competitions, and artistic competitions) which likely varied in perceived difficulty, it is possible

that the relative impact of the intent-to-outcome lay belief on forecasts shifts as information


about a competition’s difficulty becomes more salient, more detailed, or more extreme. We

encourage future research to investigate these possibilities.

Relatedly, the behavior prediction literature also reveals that information about an

individual’s abilities, characteristics, and past achievements can also influence forecasts of their

future outcomes (Burns & Corpus, 2004; Camerer, 1989; Heyman & Gelman, 1998; Rholes &

Ruble, 1984; Skowronski & Carlston, 1987; Vallone & Tversky, 1985); thus, it is possible that

such information may further moderate the impact of the intent-to-outcome lay belief on

observers’ forecasts. Of note, the current research finds that the intent-to-outcome lay belief

continues to guide forecasts when forecasters are aware of some relevant historical performance

information (e.g., the number of times a contestant has won similar prior competitions; Studies

1B–1C), as well as when forecasters are aware of a contestant’s social category membership

(e.g., membership in social categories demarcated by race, age, gender, and disability status).

Such information about others’ social category membership influences perceptions of those

others’ traits and abilities (as well as those others’ future behaviors; Abreu 1999; Cuddy et al.,

2008; Ford, 1997; Hurwitz & Peffley, 1997; Plant, Goplen, & Kunstman, 2011; Sagar &

Schofield, 1980); therefore, the current research provides initial evidence that the intent-to-

outcome lay belief guides forecasts even when forecasters perceive that they have some

information regarding others’ traits and abilities. These findings are consistent with significant

literature which finds that lay theories often guide hypothesis testing of relevant information,

particularly when such information is ambiguous, inconclusive, or subjective (as was likely

perceived to be the case in the current studies; Darley & Gross, 1983; Gibson et al., 1997;

Higgins, Rholes, & Jones, 1977). Nevertheless, a large amount of unambiguous contestant-

relevant information may affect the degree to which the intent-to-outcome lay belief guides


forecasts. We encourage future research to investigate how such information interacts with the

intent-to-outcome lay belief in determining which hypothesis forecasters spontaneously test.

Also of importance, the intent-to-outcome lay belief may originate from the pervasive

salience of others’ intentions that has been frequently documented in Western cultures (Aarts,

Gollwitzer, & Hassin, 2004; Hassin, Aarts, & Ferguson, 2005; Iacoboni et al., 2005; Liepelt,

Cramon, & Brass, 2008). This focus on an individual’s intentions might more reliably emerge in

individualistic (e.g., Western) cultures, in which individual ambition is paramount (Bergeron &

Schneider 2005; Boreham, 2004; Schwartz, 1999). While the present work employed samples

that included participants with a broad range of ages and geographic locations within the United

States, it is possible that the current findings may be less likely to emerge in collectivistic

cultures in which individual ambition and intentions are less salient. Future work should examine

whether cultural interdependence moderates the findings documented here, or whether different

patterns of results emerge among different cultures.

Conclusion

As social animals, people frequently predict not only their own future, but also the future

of self-irrelevant others in a wide range of contexts, spanning from academics and athletics, to

entertainment and legal events. However, the forecasting literature has remained relatively silent

on how observers make forecasts about self-irrelevant others. In this research, we find that when

observers formulate predictions about another individual’s competitive efforts, observers

spontaneously test the hypothesis that the individual’s intended outcome will occur, which biases

observers to overestimate the likelihood that it will. This bias originates from the application of a


previously undocumented lay belief—that others generally achieve their intentions—to

probabilistic assessments.


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Table 1. List of Supplementary Studies.

Supplementary Materials Topic Evidence Page

Supplementary Materials D Documenting a generalized forecasting bias

This study finds that the bias persists when people forecast the competitive outcome that will be experienced by an African American female.

p. S5

Supplementary Materials E Documenting the robustness of the forecasting bias to measurement order

This study finds that the bias persists when the order in which participants complete the forecasting measure and the selective hypothesis testing measure is counterbalanced

p. S7

Supplementary Materials G Examining alternative explanations

This study finds evidence inconsistent with the possibility that just world beliefs or self-efficacy underlies the current phenomenon

p. S10


Figure 1. Mediation of Condition on Forecasts in Study 2A (First Panel) and Study 2B (Second Panel)

Notes. The path coefficients are unstandardized betas. Values in parentheses indicate the effect of condition on forecasts after controlling for the mediator. *p < .05 **p < .01 ***p < .001

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And the Winner Is…? Forecasting the Outcome of Others ...expectations and lay beliefs (e.g., Lord, Lepper, & Preston, 1984; Sanbonmatsu et al., 1998), we further propose that this