# Both better and worse than others depending on difficulty: Replication and extensions of Kruger’s (1999) above and below average effects
Max Korbmacher∗ Ching (Isabelle) Kwan† Gilad Feldman‡

## Abstract

Above-and-below-average eﬀects are well-known phenomena that arise when comparing oneself to others. Kruger (1999) found that people rate themselves as above average for easy abilities and below average for diﬃcult abilities. We conducted a successful pre-registered replication of Kruger’s (1999) Study 1, the ﬁrst demonstration of the core phenomenon (N = 756, US MTurk workers). Extending the replication to also include a between-subject design, we added two conditions manipulating easy and difﬁcult interpretations of the original ability domains, and with an additional dependent variable measuring perceived diﬃculty. We observed an above-average-eﬀect in the easy extension and below-average-eﬀect in the diﬃcult extension, compared to the neutral replication condition. Both extension conditions were perceived as less ambiguous than the original neutral condition. Overall, we conclude strong empirical support for Kruger’s above-and-below-average eﬀects, with boundary conditions laid out in the extensions expanding both generalizability and robustness of the phenomenon. Keywords: above-average eﬀect, below-average eﬀect, bias, anchoring, egocentrism
∗Co-ﬁrst-author. Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway, https://orcid.org/0000-0002-8113-2560.
†Co-ﬁrst-author. Department of Psychology, University of Hong Kong, Hong Kong SAR. ‡Corresponding author. Department of Psychology, University of Hong Kong, Hong Kong SAR. https:// orcid.org/0000-0003-2812-6599. Email: gfeldman@hku.hk.
We would like to thank Leo Chan for reviewing the materials during an early project stage and Raj Aiyer, Hirotaka Imada, Matan Mazor, Nicole Russel, Burak Tunca, and Meng-Yun Wang for reviewing the manuscript prior to submission. Their work led to many helpful comments, which improved the project output substantially. We would also like to thank Prasad Chandrashekar for his help with mixed modelling.
All materials, data, and code are available in the OSF supplement at https://osf.io/7yfkc/. Copyright: © 2022. The authors license this article under the terms of the Creative Commons Attribution 3.0 License.

## Introduction

### 1.1 Background

The above-average eﬀect refers to the tendency to perceive oneself as better than the average person across diﬀerent aspects. Kruger (1999) was the ﬁrst to present instances of the opposite – a below-average eﬀect – the tendency to view oneself as worse than the average person, and he proposed that this opposing eﬀect depends on the diﬃculty of the ability domain. The above-average eﬀect was observed when self-perceived skills in an ability domain were high, whereas the below-average eﬀect occurred when self-perceived skills were low. Hence, Kruger identiﬁed the two eﬀects’ underlying mechanism to be the egocentric nature of comparative ability judgments and suggested an anchoring-andadjustment account. Individuals anchor onto their own skills and then adjust away from their own anchor when judging the skill of others. Therefore, when considering easy activities, people perceive their ability/skill as high and display the above-average eﬀect, thus failing to account for the “true” distribution curve of such abilities/skills which includes others who are also highly skilled. When activities are diﬃcult and hence absolute domain ability is generally low, a below-average eﬀect results from the failure to consider that others are also not highly skilled.
This result was ﬁrst operationalized in Study 1 in Kruger (1999) using a questionnaire in which participants ﬁrst compared themselves with their peers on four relatively easy and four relatively diﬃcult ability domains (or activities). Participants then answered a series of questions concerning: 1) estimates of their own and classmates’ absolute abilities (termed “comparative ability”); 2) desirability; 3) ambiguity of each ability; and 4) past experience of each ability. A strong negative correlation between domain diﬃculty and participants’ comparative ability judgments supported both above and below-average eﬀects (Kruger, 1999). The study demonstrated correlational evidence for the egocentric nature of comparative ability judgments, in the form of a strong positive correlation between participants’ ratings of their own and their comparative abilities. For all ability domains, participant judgments of their own absolute abilities better predicted their comparative ability judgments than did participants’ judgments of their peers’ skills. Additional experimental studies (2 and 3 in Kruger, 1999) used a situation in which participants received either a very easy or a diﬃcult test, leading to similar results as in Study 1. The anchoring-and-adjustment account was deemed consistent with the fact that cognitive load increased bias during comparative ability judgments.
We conducted a close replication and extensions of Kruger (1999) with two main goals; 1) test the robustness of above- and below-average eﬀects, and 2) examine extensions to test whether ambiguities regarding domain diﬃculty may moderate this eﬀect. Two between-subject conditions were added to the original design to test whether an easier or more diﬃcult version of Kruger’s original ability domains would moderate the eﬀects. Furthermore, we added an additional dependent variable to assess the phenomenon using

ratings of perceived domain diﬃculty more directly. We begin by introducing the literature on above-and-below-average eﬀects and the choice of target article for replication, then provide information on the original ﬁndings, and outline our added extensions.

### 1.2 Above-and-below-average eﬀects

In the 1980s, researchers began to assess subjects’ self-evaluations in relationship to their peers with the results showing over-estimations of own chances for positive outcomes compared to the average population (e.g., Weinstein, 1980, 1983). Focusing on comparisons with others, the phenomenon became later known as above or better-than-average eﬀect (Kruger, 1999). Research picked up quickly on the above-average eﬀect, testing boundary conditions such as culture (Heine & Lehman, 1997) or self-appraisal (Wilson & Ross, 2001). Kruger (1999) was the ﬁrst to add that there is not only an above- but also below-average eﬀect.

### 1.2.1 Underlying mechanisms

Throughout the last decades, a range of diﬀerent underlying mechanisms was proposed to explain the above-average eﬀect (less research focused on the below-average eﬀect), such as informational diﬀerences (i.e., knowing more about oneself than others), focalism (i.e., focussing on oneself during comparative judgments), naïve realism, and egocentrism (Brown, 2012). The ﬁnal mechanism was also used in the chosen study for replication (Kruger, 1999); when people assess how they compare with their peers, they may focus egocentrically on their own skills and insuﬃciently account for the skills of the comparison group. However, Kruger (1999) reported not only an above-average eﬀect, but also a below-average eﬀect, both explained by egocentrism.

### 1.2.2 Theoretical grounding

Originally, the above-average eﬀect has been described as motivated by self-enhancement needs (i.e., to induce positive aﬀect towards oneself) or a byproduct of motivated reasoning (Alicke, 1985; Brown, 1986; Kunda, 1990; Taylor & Brown, 1988). Self-enhancement enables the maintenance of a global self-concept allowing for both positive attributes under personal control and negative attributes resulting from factors beyond personal control (Alicke, 1985). Self-veriﬁcation can be used as another explanation for the above-average eﬀect (Zell et al., 2020). Expanding on self-enhancement, the self-veriﬁcation theory describes that both self-enhancement and exposure to information which creates and strengthens a biased view of oneself can lead to phenomena such as the above-and-below-average
See Ziano et al. (2021) for a recent successful direct replication of Alicke (1985), showing that people rate more desirable traits to be more descriptive of themselves than of others, and extending that the eﬀect was stronger for more controllable traits. This study was diﬀerent from the current work as it focused on traits whereas the focus here is on skills.

eﬀects (Zell et al., 2020). In that sense, higher self-esteem has been linked with stronger above-average eﬀects (e.g., Bosson et al., 2000; Chung et al., 2016). Support for the motivational perspective and the ubiquity of the above-average eﬀect was provided by those objectively being below-average in certain characteristics displaying the above-average effect (e.g., Sedikides et al., 2014). For instance, prisoners comparing themselves with non-prisoners on pro-social characteristics rated themselves as above-average in most characteristics (Sedikides et al., 2014). Another explanation can be found in social comparisons during which people evaluate their social position compared to relevant peers – with the tendency of positioning oneself as higher-standing (Gerber et al., 2018). An example of both eﬀects applying during social comparisons is when Democrats and Republicans compare their own warmth and competency with the average person of their in- and outgroup (Eriksson & Funcke, 2013). In-group comparisons lead to below-average ratings for warmth among Democrats and above-average eﬀects among Republicans, which reversed for outgroup comparisons (Eriksson & Funcke, 2013). Above-and-below-average eﬀects have also been found to vary across ages, with egocentrism accounting for age diﬀerences (Zell & Alicke, 2011). Young, middle-aged, and older adults displayed an above-average eﬀect for most ability and trait dimensions, whereas a below-average eﬀect was observed for older adults with clear deﬁciencies (Zell & Alicke, 2011).

### 1.2.3 Follow-up research

Due to the large number of citations of Kruger’s (1999) ﬁndings, it is diﬃcult to generalize the publication’s impact. However, focusing on follow-up research on the above and below-average eﬀects’, more recent studies provided information about the eﬀects’ wide applicability and boundary conditions, with a large body of work supporting the original ﬁndings (e.g., Aucote & Gold, 2005; Burson et al., 2006; Johansson & Allwood, 2007; Sweeny & Shepperd, 2007). For example, building on the original ﬁndings, Giladi and Klar (2002) demonstrated that individual items within a positive group tend to be rated as aboveaverage and individual items within a negative group tend to be rated as below-average. These eﬀects can be reversed depending on the timing of the denotation of the target item, which aﬀects the direction and size of the comparative biases (Windschitl et al., 2008b).
Much subsequent research also continued to explore underlying mechanisms, such as motivations and debiasing factors inﬂuencing egocentrically biased comparative judgments. Epley and Caruso (2004) discussed how unconscious, automatic features of human judgment result in egocentric judgments that appear objective to the judges themselves. Windschitl et al.’s (2008a) experiments attempting to debias over-optimism for easy tasks and underoptimism for hard tasks through feedback was only successful under restrictive conditions. Yet, their results support the pervasiveness of egocentric biases as participants failed to generalize non-egocentric tendencies to new contexts.

### 1.3 Choice of study for replication

Kruger’s (1999) work made an important contribution to the ﬁeld by introducing the belowaverage eﬀect and conditions in which occurs, which adds to the understanding of a highly prevalent eﬀect with importance to daily reasoning. A recent meta-analysis of better-thanaverage-eﬀect studies found the eﬀect to be robust across studies, yet, with the eﬀect being smaller for abilities compared to personality traits (Zell et al., 2020). Problematically, deﬁnitions and measurement of skill are incongruent which leads to biased assessment and operationalizations diﬀer strongly between studies testing above-and-below averageeﬀects, generally (Zell et al., 2020), and in speciﬁc contexts such as drivers’ overconﬁdence in their driving skills (Sundström, 2008). Hence, despite the proliﬁc literature that followed, the above-average eﬀect’s robustness has been repeatedly called into question (Sundström, 2008; Zell et al., 2020).
However, some studies failed to conceptually replicate mechanisms and boundary conditions originally reported by Kruger, such as the relationship of estimates about others in relationship to estimates about oneself. For example, Moore and Kim (2003) found mixed evidence for the relationship between comparative ability and the evaluations of others’ ability. This was also shown in a practical context by Walsh and Ayton (2009). After presenting an imaginary scenario in which a doctor provides information about a serious diagnosis applying to the participant and how that aﬀects others’, own happiness estimates by participants were indeed inﬂuenced by information about others’ happiness.
We chose Kruger’s (1999) study for replication based on the following factors: impact, open questions about boundary conditions of the above and below-average eﬀects, and absence of direct replications. To the best of our knowledge, no direct replications of Kruger (1999) have been publshed. Yet, the article has had a signiﬁcant impact on several scientiﬁc and practical ﬁelds, including management (Bazerman & Moore, 2012), economy (DellaVigna, 2009; Koellinger et al., 2007), medicine (Stewart et al., 2013), education, or the workplace in general (Dunning et al., 2004). At the time of writing (May 2021), there were 1178 Google Scholar citations of the article and many important follow-up theoretical and empirical articles (Chambers & Windschitl, 2004; Moore, 2007; Moore & Cain, 2007; Moore & Small, 2007; Whillans et al., 2020; Windschitl et al., 2008b). We chose Study 1, as it was the ﬁrst demonstration of the core phenomenon. We aimed to revisit this classic phenomenon in a well-powered preregistered close replication (e.g., Brandt et al., 2014).

### 1.4 Original hypotheses in target article

In the original study, participants compared themselves to their peers on eight ability domains of varying diﬃculty. Kruger proposed that (Horig1:) compared to judgments of their peers’ abilities, people’s judgments of their own abilities account for more variance in their comparative ability judgments.

Past research on reasons for people’s tendency to focus on their own ability when comparing themselves to others oﬀers insight on why comparative ability judgments are egocentric in nature. One’s own skills are more likely to be assessed ﬁrst when comparing the self to others (Srull & Gaelick, 1983), are easier to conceptualize than skills of the average person (Higgins et al., 1982; Higgins & Bargh, 1987; Srull & Gaelick, 1983), and have a larger database to refer to than others’ skills (Ross & Sicoly, 1979). These explanations formed the basis of Kruger’s primary hypothesis. When comparing one’s own ability to peers’ ability, assessments are predominantly based on the perception of one’s own skills and less on the perceptions of peers’ skills, and therefore, perceptions of one’s own absolute ability better predict comparative ability judgments.
Based on that, Kruger proposed that (Horig2:) people tend to perceive themselves as above average when considering easy abilities, and that (Horig3:) people tend to perceive themselves as below average when considering diﬃcult abilities. We merged the dichotomized hypotheses to propose that the more diﬃcult the ability domain is perceived to be, the more likely a person is to shift from perceiving oneself as above average to perceiving oneself as below average.

### 1.5 Original ﬁndings in target article

Kruger (1999) used a combination of correlational studies, one-sample t-tests, and multiple regression and found support for all hypotheses (Table 1). Above and below-average eﬀects were prevalent for all but one diﬃcult item: telling jokes. He observed an inverse association between the domain diﬃculty and comparative ability: as ability domains increased in diﬃculty, the perception of their comparative ability decreased. Participants believed to be above average for easy abilities and below average for diﬃcult abilities.
To examine the relationship between one’s own absolute ability and comparative ability judgments, we conducted multiple regressions predicting comparative ability from their own ability, and others’ ability for each of the eight abilities. Participants’ perception of their own ability better predicted their comparative ability judgments. Participants anchored onto their own absolute ability, as opposed to their peers’ absolute ability when comparing themselves to others across ability domains. Here we summarize eﬀect sizes and power analysis for the original study results in the sections “eﬀect size calculations of the original study eﬀects” and “power analysis of original study eﬀect to assess required sample for replication” in the OSF supplement.

### 1.6 Extensions to the Original Study Design

1.6.1 Extension 1: Manipulating domain diﬃculty
We aimed to extend the replication study by considering the ambiguities in the deﬁnitions of easy and diﬃcult used in the domains of the original study. The ability domains in the target article were only succinctly described (see Table 2). Each ability domain may connote

### Table 1
*Kruger’s (1999) ﬁndings: Mean comparative ability estimates and judgmental weight of own and peers’ abilities.*

<table>
  <thead>
    <tr>
      <th>Ability</th>
      <th>Domain<br>diﬃculty<br>1</th>
      <th>Comparative<br>ability<br>2</th>
      <th>Judgmental weight<br>of Own ability<br>3</th>
      <th>Judgmental weight<br>of Others’ ability<br>3</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="5"><strong>Easy</strong></td></tr>
    <tr>
      <td>Using a mouse</td>
      <td>3.1</td>
      <td>58.8∗∗</td>
      <td>0.21</td>
      <td>0.06</td>
    </tr>
    <tr>
      <td>Driving</td>
      <td>3.6</td>
      <td>65.4∗∗∗∗</td>
      <td>.89∗∗∗∗</td>
      <td>–.25∗</td>
    </tr>
    <tr>
      <td>Riding a bicycle</td>
      <td>3.9</td>
      <td>64.0∗∗∗∗</td>
      <td>.61∗∗∗∗</td>
      <td>–0.02</td>
    </tr>
    <tr>
      <td>Saving money</td>
      <td>4.3</td>
      <td>61.5∗∗</td>
      <td>.90∗∗∗∗</td>
      <td>–.25∗∗∗</td>
    </tr>
    <tr><td colspan="5"><strong>Diﬃcult</strong></td></tr>
    <tr>
      <td>Telling jokes</td>
      <td>6.1</td>
      <td>46.5</td>
      <td>.91∗∗∗∗</td>
      <td>–0.03</td>
    </tr>
    <tr>
      <td>Playing chess</td>
      <td>7.1</td>
      <td>27.8∗∗∗∗</td>
      <td>.96∗∗∗∗</td>
      <td>–.22∗∗</td>
    </tr>
    <tr>
      <td>Juggling</td>
      <td>8.3</td>
      <td>26.5∗∗∗∗</td>
      <td>.89∗∗∗∗</td>
      <td>–0.16</td>
    </tr>
    <tr>
      <td>Programming</td>
      <td>8.7</td>
      <td>24.8∗∗∗∗</td>
      <td>.85∗∗∗∗</td>
      <td>–0.1</td>
    </tr>
  </tbody>
</table>

∗p < .05. ∗∗p < .01. *** p< .001. ∗∗∗∗p < .0001.

diﬀerent meanings, depending on how participants interpret the domains. For instance, the ability “saving money” was categorized as an easy ability. Yet, the amount of money saved was not speciﬁed, and that may matter for perceived diﬃculty, as saving 3% of income per month is likely to be perceived as easier than saving 20% of income per month.
Therefore, we manipulated domain diﬃculty. In our replication, we randomly assigned participants to one of the three conditions receiving diﬀerent deﬁnitions of the ability domains, either: 1) original domain condition (replication); 2) easy domain condition (extension) with an easy reinterpretation of the original domains; or 3) diﬃcult domain condition (extension) with a diﬃcult reinterpretation of the original domains (Table 2).
For the two extension groups, the extension domains aim to be speciﬁcally deﬁned in measurable terms. More context is provided for the domains to be more speciﬁc, such as the hand used (dominant versus non-dominant hand) for using a mouse, the location and type of car (home country and automatic gear car versus foreign country and manual gear car) for driving, and the help received for computer programming (someone very knowledgeable versus someone not very knowledgeable), which is an ability domain most participants may not have experience with. Additionally, an objective measure should be

### Table 2
*Extension: Manipulation of perceived domain diﬃculty in target’s domains.*

<table>
  <thead>
    <tr>
      <th>Original domain group<br>Easy domain group</th>
      <th>Diﬃcult domain group</th>
    </tr>
    <tr>
      <th>(replication)(extension)</th>
      <th>(extension)</th>
    </tr>
    <tr>
      <th>Easy domains</th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Using a mouse<br>Using a mouse with your</td>
      <td>Using a mouse with your</td>
    </tr>
    <tr>
      <td>dominant hand</td>
      <td>non-dominant hand</td>
    </tr>
    <tr>
      <td>Driving<br>Driving a car with automatic</td>
      <td>Driving a car with manual gear</td>
    </tr>
    <tr>
      <td>gear in your home country</td>
      <td>in a foreign country where<br>people drive on the opposite<br>side of the road</td>
    </tr>
    <tr>
      <td>Riding a bicycle<br>Riding a bicycle for 10 minutes</td>
      <td>Riding a bicycle for an hour up</td>
    </tr>
    <tr>
      <td>on a ﬂat road</td>
      <td>a road with an upwards incline</td>
    </tr>
    <tr>
      <td></td>
      <td>slope</td>
    </tr>
    <tr>
      <td>Saving money<br>Saving 3% of your income each</td>
      <td>Saving 20% of your income</td>
    </tr>
    <tr>
      <td>month</td>
      <td>each month</td>
    </tr>
    <tr>
      <td>Diﬃcult domains</td>
      <td></td>
    </tr>
    <tr>
      <td>Telling jokes<br>Telling a joke to one person</td>
      <td>Telling a joke in front of a live</td>
    </tr>
    <tr>
      <td>you know well (e.g., friend,</td>
      <td>audience in an improv stand-up</td>
    </tr>
    <tr>
      <td>family member, etc.)</td>
      <td>comedy club</td>
    </tr>
    <tr>
      <td>Playing chess<br>Win a game of chess against an</td>
      <td>Win a game of chess against an</td>
    </tr>
    <tr>
      <td>AI (computer) in beginners’</td>
      <td>AI (computer) in advanced</td>
    </tr>
    <tr>
      <td>mode</td>
      <td>mode</td>
    </tr>
    <tr>
      <td>Juggling<br>Juggling 2 balls</td>
      <td>Juggling 4 balls</td>
    </tr>
    <tr>
      <td>Programming<br>Programming guided by</td>
      <td>Programming guided by</td>
    </tr>
    <tr>
      <td>someone very knowledgeable</td>
      <td>someone not knowledgeable in</td>
    </tr>
    <tr>
      <td>in programming</td>
      <td>programming</td>
    </tr>
  </tbody>
</table>

quantitatively determined in units that can be measured (e.g., length of time, amount of money) or counted (e.g., number of people; Roth et al., 2008). Therefore, the extension domains also use criteria such as time (10 minutes versus 1 hour), number of people (one person versus a live audience in an improv stand-up comedy club), and diﬃculty (beginner mode versus advanced mode).
1.6.2 Extension 2: Measuring domain diﬃculty
For the second extension, we added an additional dependent variable measuring domain diﬃculty. In the original study, domain diﬃculty was determined in a pretest by a separate group of participants (n = 39). They rated their absolute ability – the extent of how skilled they are – on the eight abilities on a 10-point scale (higher number indicates higher

skill level): “For this ability, please rate your own ability from 1 (very unskilled) to 10 (very skilled)“. The ratings were then reverse-scored and higher numbers indicated greater domain diﬃculty. The four ability domains lower than the midpoint of the scale were categorized as easy domains, whereas the four ability domains higher than the midpoint of the scale were categorized as diﬃcult domains.
Due to problems associated with categorizing the continuous variable of the diﬃculty level of ability domains into easy domains or diﬃcult domains, in the current replication, we measured domain diﬃculty on a continuous scale: “Please rate the diﬃculty of this ability from 1 (very easy) to 10 (very diﬃcult)”. Details on the adjustment can be found in the section below “adjustments to the original study”. In contrast to the original study, domain diﬃculty ratings were scored on a similar scale as comparative ability, (own and others’) comparative ability, desirability, and ambiguity.
We examined diﬃculty ratings across all domains to assess whether perceived diﬃculty was as expected in the original and conditions in which diﬃculty was manipulated. For the easy domain condition, we hypothesized that easy interpretations of the original domains would result in lower domain diﬃculty ratings across all abilities compared to ratings of the original domain group. For the diﬃcult domain condition, we hypothesized that diﬃcult interpretations of the original domains would result in higher domain diﬃculty ratings across all abilities compared to original domain group ratings. We expected the ambiguity ratings for both easy and diﬃcult conditions to be lower than that in the original’s domains. Additionally, we tested whether comparative ability would be inﬂuenced by our easy/diﬃcult manipulations.

### 1.7 Hypotheses

Based on the original study and the current extension hypotheses, this replication aims to test four central hypotheses (Table 3).

### 1.8 Adjustments to the original study

In the original study, the eight ability domains were divided into two categories: four easy domains and four diﬃcult domains. On a 10-point scale from very easy to very diﬃcult, easy domains had domain diﬃculty ratings below 5 (the midpoint of the scale), and diﬃcult domains above 5, respectively. The above-average eﬀect was tested for the easy domains, whereas the below-average eﬀect was tested for the diﬃcult domains.
Yet, several issues may arise from treating continuous variables as categorical. First, the categorization of continuous variables, especially dichotomization of placing variables into two groups, might lead to misclassiﬁcations, loss of information and power (Naggara
Although this test was the reason for the preregistration, due to an error, neither hypotheses or tests related to the core questions of the extensions were part of the preregistration. Hence, analyses connected to this question in the extension will be treated as exploratory.

### Table 3
*Summary of the hypotheses.*

<table>
  <thead>
    <tr>
      <th>Hypothesis</th>
      <th>Statement</th>
      <th>Variables</th>
      <th>Conditions</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>H1<br>(Original)</td>
      <td>Compared to judgments of others’<br>abilities, participant judgments of<br>their own abilities better predict<br>their comparative ability<br>judgments.</td>
      <td>Own absolute<br>ability; others’<br>absolute ability;<br>comparative<br>ability</td>
      <td>Replication and<br>extension<br>conditions</td>
    </tr>
    <tr>
      <td>H2<br>(Original reframed)</td>
      <td>The more diﬃcult the ability<br>domain, the more likely a person<br>is to shift from perceiving oneself<br>as above average to perceiving<br>oneself as below average.</td>
      <td>Comparative<br>ability; domain<br>diﬃculty;<br>desirability;<br>ambiguity</td>
      <td>Replication and<br>extension<br>conditions</td>
    </tr>
    <tr>
      <td>H3<br>(Extension)</td>
      <td>Compared to the replication<br>condition participants, the easy<br>domain condition participants<br>assign lower domain diﬃculty and<br>ambiguity ratings to abilities.</td>
      <td>Domain<br>diﬃculty;<br>ambiguity</td>
      <td>Replication and<br>easy domain<br>conditions</td>
    </tr>
    <tr>
      <td>H4<br>(Extension)</td>
      <td>Compared to the replication<br>condition, the diﬃcult domain<br>condition participants assign</td>
      <td>Domain<br>diﬃculty;<br>ambiguity</td>
      <td>Replication and<br>diﬃcult domain<br>conditions</td>
    </tr>
  </tbody>
</table>

et al., 2011). Second, the loss of power by dichotomizing variables at the median is equal to discarding one-third of the data (Cohen, 1983; MacCallum et al., 2002). Third, variation between categorized groups may be underestimated as close response scores divided into diﬀerent groups are deﬁned as being very diﬀerent instead of very similar. It has thus been suggested to keep variables continuous using methods such as linear regressions instead of t-tests (Altman & Royston, 2006).
For the above reasons, we did not assign ability domains to speciﬁc dichotomic easy/diﬃcult categories. The above- and below-average-eﬀects were tested on a continuous scale: instead of using one-sample t-tests, correlations were used to test the relationship between domain diﬃculty and comparative ability in three diﬀerent ways: item-wise, compiled items in a vector (but not averaging across them), and row-wise averaged for the three conditions. Applying this method is a more direct assessment of perceived diﬃculty with the same sample. For a full overview of diﬀerences between the current and the original study see the OSF supplement, section “Comparisons and deviations”.

### 1.9 Pre-registration and open science

Before data collection, the experiment was pre-registered (see the OSF supplement). Preregistrations, power analyses, materials, data, exclusions, manipulations, power analyses, and other details and disclosures are available in the OSF supplement. Data collection was completed before analyses.

## Method

### 2.1 Participants and power analyses

We conducted power analyses in R using the pwr package (Champely et al., 2018). The power analyses suggested a sample size of 160 to be suﬃcient for reaching 95% power with an alpha-level = .05 assuming an eﬀect size of f = 0.099 (informed by Kruger, 1999) for a 2-factor multiple linear regression analysis (see OSF supplement, section “Power analysis of original study eﬀect to assess required sample for replication”). We tried to exceed this estimate (following replication recommendation such as Simonsohn, 2015) and added extensions thereby leading to the recruitment of 756 Amazon MTurkers. A total of 65 participants failed to meet the pre-registered inclusion criteria and were excluded, resulting in a total of 691 included participants (see Table1 in the OSF supplement for sample comparison and exclusion details).

### 2.2 Design

The original study used a within-subject design with one-sample analyses conducted for each condition (easy versus diﬃcult domains), yet in the current replication, we used a 3 (between diﬃcult conditions: original, easy, diﬃcult) x 2 (within diﬃculty conditions: easy, diﬃcult) mixed-design. All participants were presented with eight items (within-subjects; see Table2). We used the same methods as in the original study for within-group analyses and added additional analyses for the between-group comparisons (see the OSF supplement for more details and full measures).

### 2.3 Procedure

Participants were recruited through MTurk on TurkPrime/CloudResearch (Litman et al., 2017) and completed questionnaires via a provided “Qualtrics” link after giving consent. Participants were randomly assigned to one of three conditions: 1) Original domains (8 original domains; 4 easy and 4 diﬃcult domains), 2) Easy domains extension (easy reinterpretations of the 8 original domains), or 3) Diﬃcult domains extension (diﬃcult reinterpretations of the 8 original domains).

### Table 4
*Comparison of original study and replication’s samples.*

<table>
  <thead>
    <tr>
      <th></th>
      <th>Kruger (1999)</th>
      <th>MTurk sample<br>(pre-exclusion)</th>
      <th>MTurk sample<br>(post-exclusion)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Sample size</td>
      <td>37</td>
      <td>756</td>
      <td>691</td>
    </tr>
    <tr>
      <td>Geographic origin US American</td>
      <td></td>
      <td>US American</td>
      <td>US American</td>
    </tr>
    <tr>
      <td>Gender</td>
      <td>8 males, 29 females</td>
      <td>442 males, 307<br>females, 7 unspeciﬁed</td>
      <td>397 males, 288<br>females, 6 unspeciﬁed</td>
    </tr>
    <tr>
      <td>Medium (location) Questionnaire</td>
      <td>(Cornell University)</td>
      <td>Computer (online)</td>
      <td>Computer (online)</td>
    </tr>
    <tr>
      <td>Compensation</td>
      <td>Course credit</td>
      <td>Nominal payment</td>
      <td>Nominal payment</td>
    </tr>
    <tr>
      <td>Year</td>
      <td>1999</td>
      <td>2020</td>
      <td>2020</td>
    </tr>
  </tbody>
</table>

Based on the categorization in the original study, of the eight ability domains, four were categorized as easy and the other four as diﬃcult (see Table2), presented in randomized order.

### 2.4 Measures

The original study had six dependent variables and the current study added an additional dependent variable of perceived domain diﬃculty. Across all conditions, the dependent variables were measured as participant ratings for each of the eight ability domains (Table 2). We computed Cronbach’s -scores for the original and extension eight-item scales, ﬁrst for all domains together, and then divided using the original’s categorization of easy and diﬃcult domains, being all >.63, all >.46, all>.47 (see the OSF supplement section “Reliability for domains across conditions”).

### 2.5 Exclusion criteria

The following exclusion criteria were pre-registered: 1) low proﬁciency of English (less than 5 on a scale of 1 to 7); 2) not being serious (less than 4 on a scale of 1 to 5); 3) correctly guessing one of the hypotheses; 4) having seen or done the survey before; 5) failure to complete the survey; and 6) not in or from the United States, to keep sample characteristics as close to the original study as possible.

## Evaluation

criteria for replication ﬁndings
We compare the replication eﬀects with the original eﬀects in the target article using the criteria set by LeBel et al. (2019) (See the OSF supplement sections “Criteria for evaluation of replications” and “Replication evaluation”).

We categorized the current replication as a “close replication” and provided details in Table5. Variables and questions were the same as in the original, with the addition of extensions and adjustments to ﬁt the MTurk sample, instead of Cornell university students.

## Results

We analyzed the data using R v3.6.3 (R Core Team, 2020), with analyses conducted both on a participant- and an item-level. To allow for a broader assessment of the data, we conducted preprocessing by both calculating mean scores (Table 6 for correlation matrices for each condition), and compiling the values for variables’ eight items (abilities) in their raw form, resulting in 8 rows per participant (see “Correlations per condition” subsection in the OSF supplement for correlation matrices for each condition). For analyses conducted on an item level, participant ratings for each of the eight abilities were examined.

### 3.1 Domain diﬃculty comparisons by conditions

We conducted paired-sample Wilcoxon tests comparing diﬃculty ratings between the grouped 4 easy and 4 diﬃcult replication/original and extension domain items and found domain diﬃculty ratings to be higher for diﬃcult abilities across all comparisons (summarized in Table7, ps < .001), supporting Kruger’s (1999) original categorization. Hence, all conditions were analyzed as in the original study, including correlations between the variables across the eight domains, and one-sample Wilcoxon-tests testing for the above-average effect in easy ability domains and the below-average eﬀect in diﬃcult ability domains (Tables 8.1–8.3 in the OSF supplement).
3.2 Replication: original domain condition
We conducted all analyses in this section on the original domain condition (n = 240).
3.2.1 H1: Relationship between absolute and comparative ability
In a linear regression model, own and others’ absolute ability ratings predicted mean comparative ability judgments (F(2, 237) = 323.9, p < .001, Radj2 = .73, 95% CI [0.68, 0.79]). However, we found support only for participants’ judgments of their own absolute ability as predictors of their comparative ability judgments ( = 0.90, t(239) = 19.93, p < .001).
On an item level, we conducted multiple regressions for each of the eight abilities to examine how participants’ estimates of both own and others’ absolute abilities predict comparative ability estimates (see Table8 for standardized betas). Own absolute abilities
T-statistics for the distinction of ability items into easy and diﬃcult were not reported in Kruger (1999). See “Additional Tables and Figures” in the OSF supplement for regression plots and tables.

Table 5: Classiﬁcation of the replication, based on LeBel et al. (2018).

Design facet Replication Details of deviation

IV operationalization DV operationalization IV stimuli
DV stimuli
DV stimuli
Procedural details Physical settings Contextual variables Replication classiﬁcation

Same Same Similar, with an added extension
Similar, with an added extension
Similar, with an added extension
Similar, with an added extension Diﬀerent Diﬀerent Close replication

IV1 ability domains is changed from one condition of 4 easy and 4 diﬃcult abilities, to 3 conditions of the replication group, the easy domain group, and the diﬃcult domain group. Participants were presented with either the original ability domains, easy interpretations of the original ability domains, or diﬃcult interpretations of the original ability domains.
An additional dependent variable, DV1 (domain diﬃculty), is added.
For DV2 (comparative ability judgment), the scale was changed from 0–99 to 0–100 for easier comprehension.
For DV2 (comparative ability judgment) and DV4 (Judgmental weight of others’ absolute abilities), the comparison group was changed from “other students from the course” to “other MTurk workers” to ensure applicability for all Mturk participants.
For DV7 (experience in the ability domain), the scale used to measure prior experience was unspeciﬁed in the original study. Similar to the majority of other dependent variables, it is measured using a scale of 1 (no experience at all) to 10 (very experienced).
Participants are all assigned to the same condition in the original study. In the replication, they are randomly assigned to one of the three conditions.
From a questionnaire to ﬁlling out an online Qualtrics survey.
From Cornell University undergraduates to American MTurk workers as participants.
With two added extensions.

were generally better in explaining changes in comparative ability judgments than others’ skills, which supports H1.

### Table 6
*Mean ratings across all abilities for the three conditions.*

<table>
  <thead>
    <tr>
      <th>Variable</th>
      <th>Original domains<br>(n = 240)<br>Mean</th>
      <th>SD</th>
      <th>Easy domains<br>(n = 225)<br>Mean</th>
      <th>SD</th>
      <th>(n = 226)<br>Mean</th>
      <th>Diﬃcult domains<br>SD</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Mean domain diﬃculty</td>
      <td>6.05</td>
      <td>1.15</td>
      <td>5.22</td>
      <td>1.63</td>
      <td>7.39</td>
      <td>1.19</td>
    </tr>
    <tr>
      <td>Mean comparative ability</td>
      <td>53.29</td>
      <td>14.5</td>
      <td>58.86</td>
      <td>14.92</td>
      <td>46.97</td>
      <td>18.86</td>
    </tr>
    <tr>
      <td>Mean own absolute ability</td>
      <td>6.04</td>
      <td>1.37</td>
      <td>6.64</td>
      <td>1.44</td>
      <td>4.77</td>
      <td>2.02</td>
    </tr>
    <tr>
      <td>Mean others’ absolute ability</td>
      <td>6.22</td>
      <td>1.14</td>
      <td>6.59</td>
      <td>1.33</td>
      <td>5.06</td>
      <td>1.79</td>
    </tr>
    <tr>
      <td>Mean desirability</td>
      <td>8.15</td>
      <td>0.99</td>
      <td>7.9</td>
      <td>1.15</td>
      <td>7.54</td>
      <td>1.35</td>
    </tr>
    <tr>
      <td>Mean ambiguity∗</td>
      <td>3.00</td>
      <td>1.24</td>
      <td>2.68</td>
      <td>1.23</td>
      <td>2.76</td>
      <td>1.43</td>
    </tr>
  </tbody>
</table>

∗ Ambiguity scores were reversed to indicate increasing ambiguity from 1 to 10.

### Table 7
*Asymptotic Wilcoxon-Mann-Whitney tests comparing perceived domain diﬃculty ratings between easy and diﬃcult abilities (within conditions).*

<table>
  <thead>
    <tr>
      <th>Condition</th>
      <th>T-statistic</th>
      <th>df</th>
      <th>Mean<br>diﬀerence</th>
      <th>p-value</th>
      <th>Eﬀect<br>size r</th>
      <th>95% CI</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Original</td>
      <td>668.5</td>
      <td>238</td>
      <td>2.78</td>
      <td>&lt;.001</td>
      <td>0.82</td>
      <td>[0.79, 0.85]</td>
    </tr>
    <tr><td colspan="7"><strong>(replication)</strong></td></tr>
    <tr>
      <td>Easy domain</td>
      <td>1416</td>
      <td>223</td>
      <td>1.99</td>
      <td>&lt;.001</td>
      <td>0.75</td>
      <td>[0.69, 0.80]</td>
    </tr>
    <tr><td colspan="7"><strong>(extension)</strong></td></tr>
    <tr>
      <td>Diﬃcult domain</td>
      <td>1917</td>
      <td>224</td>
      <td>1.22</td>
      <td>&lt;.001</td>
      <td>0.69</td>
      <td>[0.62, 0.75]</td>
    </tr>
    <tr><td colspan="7"><strong>(extension)</strong></td></tr>
  </tbody>
</table>

For the relationship between absolute and comparative ability ratings across all abilities (240 participants * 8 items), we found a strong relationship between comparative ability estimates and others’ ability ratings (r(6) = 0.94, p < .001, 95% CI [0.71, .99]); and between comparative ability estimates and own ability ratings (r(6) = 0.99, p < .001, 95% CI [0.96, .99]). Hotelling’s (1940) t indicated these correlations to be diﬀerent from each other (t(5) = 4.66, p = .006).
3.2.2 H1: Additional correlation analyses for the relationship between absolute and comparative ability
When adding two modes of analysis, namely, vector-compiled scores and inventory mean scores , Pearson’s rs, calculated for vector-compiled scores of comparative ability estimates
Vector-compiled scores were each participant (in the replication condition) scores in all 8 domains lined up in one vector with 8 (domains) * 240 (participants) = 1920 rows. Inventory mean scores were calculated by

### Table 8
*Replication condition: Mean comparative ability estimates and judgmental weight of own versus peers’ abilities.*

<table>
  <thead>
    <tr>
      <th>Ability</th>
      <th>Domain<br>diﬃculty<br>1</th>
      <th>Percentile<br>estimate<br>2</th>
      <th>Judgment weight:<br>Own ability<br>3</th>
      <th>Judgment weight:<br>Others’ ability<br>3</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Using mouse</td>
      <td>2.70 (2.63)</td>
      <td>71.2∗∗∗ (17.90)</td>
      <td>0.29∗∗∗</td>
      <td>0.04</td>
    </tr>
    <tr>
      <td>Driving</td>
      <td>5.19 (2.41)</td>
      <td>65.2∗∗∗ (22.08)</td>
      <td>0.85∗∗∗</td>
      <td>–0.11∗∗</td>
    </tr>
    <tr>
      <td>Riding bicycle</td>
      <td>4.14 (2.44)</td>
      <td>61.0∗∗∗ (20.48)</td>
      <td>0.76∗∗∗</td>
      <td>–0.06</td>
    </tr>
    <tr>
      <td>Saving money</td>
      <td>6.63 (2.08)</td>
      <td>62.9∗∗∗ (21.10)</td>
      <td>0.79∗∗∗</td>
      <td>–0.05</td>
    </tr>
    <tr>
      <td>Telling jokes</td>
      <td>6.10 (2.06)</td>
      <td>52.4 (22.63)</td>
      <td>0.75∗∗∗</td>
      <td>0.04</td>
    </tr>
    <tr>
      <td>Playing chess</td>
      <td>7.74 (1.75)</td>
      <td>41.0∗∗∗ (27.00)</td>
      <td>0.82∗∗∗</td>
      <td>–0.03</td>
    </tr>
    <tr>
      <td>Juggling</td>
      <td>7.64 (1.97)</td>
      <td>32.0∗∗∗ (27.67)</td>
      <td>0.59∗∗∗</td>
      <td>0.18∗∗</td>
    </tr>
    <tr>
      <td>Programming</td>
      <td>8.29 (1.74)</td>
      <td>40.7∗∗∗ (29.22)</td>
      <td>0.83∗∗∗</td>
      <td>–0.06</td>
    </tr>
  </tbody>
</table>

Note: Tablepresented as in original study (Kruger, 1999, Table 2) encompassing descriptive statistics, one-sample t-tests, and regressions. 1 Mean (SD) scores for item-wise domain diﬃculty. Higher numbers reﬂect greater diﬃculty. 2Mean (SD) scores for item-wise comparative ability/percentile estimates. Scores above 50 reﬂect an above-average eﬀect, estimates below 50 reﬂect a below-average eﬀect. See supplementary tables 8.1 and 9.1 for test statistics and CI’s. 3Standardised betas from multiple regressions predicting participants’ comparative ability (percentile) estimates from own absolute ability and peers’ absolute ability, respectively.
∗∗p < .01. ∗∗∗p < .001.

and other’s absolute ability, were r(1918) = 0.50 (95% CI [0.46, 0.53]); and between comparative ability estimates and own absolute ability were r(1918) = 0.81 (95 % CI [0.79, 0.82]); with these correlations being diﬀerent from each other (Hotelling’s (1940) t(1917) = 27.61, p < 0.001). For inventory mean scores, correlations between comparative ability estimates and other’s absolute ability were r(238) = 0.53 (p < .001, 95% CI [0.43, 0.62]); and between own and comparative ability r(238) = 0.85 ( p < .001, 95% CI [0.82, 0.89]); with these correlations being diﬀerent from each other (Hotelling’s t(237) = 11.75, p < 0.001).
However, when using a mixed-eﬀects model with random intercepts at the level of participants to explain comparative ability, positive changes in own ability explained positive changes in comparative ability and the relationship between others’ and comparative ability being the opposite (Table 9). The ﬁndings from both replicated and the new analyses present strong support for H1.
averaging the 8 domains for each participant (row-wise), resulting in 240 rows. P-values for vector-compiled scores correlations are not provided as those do not account for repeated responses of the same person.

### Table 9
*Estimated ﬁxed-eﬀects coeﬃcients of the mixed-eﬀects regression model with changes in Comparative Ability explained by Others’ and Own Ability.*

```
Predictors
B
S.E.
CI
p
(Intercept) Own Ability Others’ Ability
12.56 7.18 –0.42
[9.95, 15.18]
< 0.001
[6.86, 7.50]
< 0.001
[–0.84, –0.01]
```

Note. The table presents the ﬁxed-eﬀects coeﬃcients with all the model predictors. See supplementary section “Mixed Models” for step-wise regression results.

3.2.3 H2: Relationship between comparative ability, domain diﬃculty, and desirability.
We conducted one-sample t-tests to examine domain-wise comparative ability ratings using the 50th percentile estimates of comparative ability to classify above and below average eﬀects (as in Kruger, 1999). Similar as in Kruger‘s (1999) ﬁndings, participants indicated to be above-average for all easy ability domains (ps < .001) and below-average for three of the four diﬃcult ability domains (ps < .001; see Table 8 column 2 for descriptive statistics, and tables 8.1 and 9.1 in the OSF supplement for test statistics and CI’s). For the above and below-average eﬀects across all abilities, we found a strong negative correlation between comparative ability estimates and domain diﬃculty (r(6) = –0.85, p = .0073, 95% CI [– 0.97 -0.37]). Item-wise comparative-ability-domain-diﬃculty correlations are provided in the supplementary under ‘Replication condition: Item-wise correlations between domain diﬃculty and comparative ability ratings for each ability domain’.
When comparing desirability ratings between easy (M = 8.731, SD = 1.01) and diﬃcult ability domains (M = 7.58, SD = 1.40), a paired-samples Wilcoxon test revealed easy abilities to be more desirable (Mdiﬀerence= 1.16, Z(238) = 9.42, p < .001, r = 0.66, 95% CI [0.59, 0.73]). One-sample Wilcoxon tests revealed that all domain-speciﬁc desirability scores were higher than the scale midpoint (ps < .001; supplementary Table 9.4). That corresponded with a strong positive relationship between comparative ability and desirability (r(6) = 0.72, p = .0448, 95% CI [0.03, 0.95]).
3.2.4 H2: Additional Analyses for the relationship between comparative ability, domain diﬃculty, and desirability.
Similarly, we found a negative association between comparative ability and domain diﬃculty ratings when using vector-compiled scores (r(1918) = –0.35, 95% CI [–0.39, –0.31]).
See Table 11 in the OSF supplement: equivalence tests 1–2. See Table 11 in the OSF supplement: equivalence tests 2–3. The presented correlation on vector compiled scores is not an optimal measure as these do not account for dependence in several measures provided by the same individual. Hence, p-values are not informative and therefore not reported.

### Table 10
*Estimated ﬁxed-eﬀects coeﬃcients of the mixed-eﬀects regression model with changes in Comparative Ability explained by Others’ and Own Ability in the Replication Con- dition.*

<table>
  <thead>
    <tr>
      <th>Predictors</th>
      <th>B</th>
      <th>S.E.</th>
      <th>CI</th>
      <th>p</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>(Intercept)</td>
      <td>8.72</td>
      <td>2.4</td>
      <td>[4.01, 13.43]</td>
      <td>&lt; .001</td>
    </tr>
    <tr>
      <td>Own</td>
      <td>7.07</td>
      <td>0.18</td>
      <td>[6.73, 7.42]</td>
      <td>&lt; .001</td>
    </tr>
    <tr>
      <td>Other</td>
      <td>–0.48</td>
      <td>0.21</td>
      <td>[–0.90, –0.06]</td>
      <td>0.025</td>
    </tr>
    <tr>
      <td>Diﬃculty</td>
      <td>–0.04</td>
      <td>0.16</td>
      <td>[–0.36, 0.28]</td>
      <td>0.817</td>
    </tr>
    <tr>
      <td>Desirability</td>
      <td>0.57</td>
      <td>0.21</td>
      <td>[0.16, 0.99]</td>
      <td>0.007</td>
    </tr>
    <tr>
      <td>Ambiguity</td>
      <td>0.12</td>
      <td>0.17</td>
      <td>[–0.21, 0.45]</td>
      <td>0.48</td>
    </tr>
  </tbody>
</table>

[–0.36, 0.28]

[0.16, 0.99]

[–0.21, 0.45]

Note. The table presents the ﬁxed-eﬀects coeﬃcients with all the model predictors. See supplementary section “Mixed Models” for step-wise regression results.

The original analysis’ methods provided support for H2. Additionally, a Simpson’s paradox can be observed when averaging all eight domains into one score over various manipulated factors for each participant and then correlating them.

3.3 Extension: Easy domain and diﬃcult domain conditions
3.3.1 Comparative ability for easy and diﬃcult items by conditions
We conducted paired-sample Wilcoxon tests comparing diﬃculty ratings between the easy and diﬃcult replication/original and extension domains and found comparative ability to be estimated higher for easy abilities across all comparisons (summarized in Table 7, all p < .001).
See Table 11 in the OSF supplement: equivalence tests 4–5. Not included in the preregistration. For an overview of all correlations between mean scores across inventories for the replication condition see Tables 3.1 and 3.2 in the OSF supplement.

3.3.2 Relationship between absolute and comparative ability
We conducted multiple linear regression analyses to test how ratings of both own and others’ ability predict comparative ability judgments across all abilities. Models in both conditions predicted variance in comparative ability judgments (Feasy(2, 222) = 246.6, p < .001, Radj2 = .69, 95% CI [0.62, 0.76]; and Fdiﬃcult(2, 223) = 342.9, p<.001, Radj2 = .75, 95% CI [0.70, 0.81]). Yet, the only signiﬁcant predictors of participants’ own absolute ability were comparative ability judgments in both the easy ( = 0.86, t(222) = 17.32, p < .001) and the diﬃcult domain condition ( = 0.90, t(223) = 15.61, p < .001).

### Table 11
*Extension conditions: Mean comparative ability estimates and judgmental weight of own and peers’ abilities by domain diﬃculty.*

<table>
  <thead>
    <tr>
      <th>Ability</th>
      <th>Easy domain condition<br>Judgmental weight<br>of own ability<br>1</th>
      <th>Judgmental weight<br>of others’ ability<br>1</th>
      <th>Diﬃcult domain condition<br>Judgmental weight<br>of own ability<br>1</th>
      <th>Judgmental weight<br>of others’ ability<br>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Using mouse</td>
      <td>0.48∗∗∗</td>
      <td>0.03</td>
      <td>0.58∗∗∗</td>
      <td>0.15∗</td>
    </tr>
    <tr>
      <td>Driving</td>
      <td>0.75∗∗∗</td>
      <td>–0.1</td>
      <td>0.78∗∗∗</td>
      <td>–0.02</td>
    </tr>
    <tr>
      <td>Riding bicycle</td>
      <td>0.65∗∗∗</td>
      <td>0.06</td>
      <td>0.79∗∗∗</td>
      <td>0.06</td>
    </tr>
    <tr>
      <td>Saving money</td>
      <td>0.81∗∗∗</td>
      <td>–0.03</td>
      <td>0.78∗∗∗</td>
      <td>–0.07</td>
    </tr>
    <tr>
      <td>Telling jokes</td>
      <td>0.70∗∗∗</td>
      <td>0.10∗</td>
      <td>0.70∗∗∗</td>
      <td>0.14∗∗</td>
    </tr>
    <tr>
      <td>Playing chess</td>
      <td>0.79∗∗∗</td>
      <td>0.02</td>
      <td>0.75∗∗∗</td>
      <td>0.01</td>
    </tr>
    <tr>
      <td>Juggling</td>
      <td>0.78∗∗∗</td>
      <td>0.05</td>
      <td>0.68∗∗∗</td>
      <td>0.05</td>
    </tr>
    <tr>
      <td>Programming</td>
      <td>0.85∗∗∗</td>
      <td>–0.03</td>
      <td>0.79∗∗∗</td>
      <td>0.03</td>
    </tr>
  </tbody>
</table>

1 Standardised betas ( ) from multiple regressions predicting participants’ comparative ability (percentile) estimates from their estimates of their own absolute ability and the absolute ability of their peers, respectively.
∗p < .05. ∗∗p < .01. ∗∗∗p < .001.

Item-wise multiple linear regression analyses showed, consistent with the original study and replication condition, that extension condition participants weighted own ability estimates stronger than others’ ability estimates when assessing their comparative abilities (Table 11). All standardized betas ( ) of own absolute abilities were positive and ps <.001 (for all abilities), whereas s of others’ absolute abilities were bi-directional and smaller.
For the easy domain condition, the correlation between own ability and comparative ability was r(6) = 0.99 (p < .001, 95% CI [0.97, 0.999]); and the correlation between others’ and comparative ability was r(6) = 0.96 (p < .001, 95% CI [0.78, 0.99]); and these correlations were diﬀerent from each other (Hotelling’s (1940) t(5) = 2.85, p = 0.037). For the diﬃcult domain condition, the correlation between own ability and comparative ability was r(6) = 0.97 (p < .001, 95% CI [0.85, 0.995]); and the correlation between others’ and

comparative ability was r(6) = 0.92 (p = .001, 95% CI [0.60, 0.99]); with weaker support found for these correlations as being diﬀerent from each other (Hotelling’s t(5) = 2.24, p = 0.075).

3.3.3 Additional Analyses: Relationship between absolute and comparative ability
The vector-compiled score correlation for the easy domain condition between own and comparative ability was r(1798) = 0.78 (95% CI [0.76, 0.80]); and between others’ and comparative ability was r(1798) = 0.47 (95% CI [0.43, 0.51]). For the diﬃcult domain condition correlations between own and comparative ability was r(1806) = 0.78 (95% CI [0.76, 0.80]); and between others’ and comparative ability was r(1806) = 0.45 (95% CI [0.41, 0.48]).
Additionally, also mixed models indicated that own ability was a better predictor of comparative ability than others’ ability (Table 12).

### Table 12
*Estimated ﬁxed-eﬀects coeﬃcients of the mixed-eﬀects regression model with changes in Comparative Ability explained by Others’ and Own Ability in the Extension Con- ditions.*

<table>
  <thead>
    <tr>
      <th>Predictors</th>
      <th>B</th>
      <th>S.E.</th>
      <th>CI</th>
      <th>p</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="5"><strong>Easy condition extension</strong></td></tr>
    <tr>
      <td>(Intercept)</td>
      <td>15.48</td>
      <td>1.3</td>
      <td>[12.94, 18.02]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Own</td>
      <td>6.56</td>
      <td>0.16</td>
      <td>[6.25, 6.88]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Other</td>
      <td>–0.04<br>Diﬃcult condition extension</td>
      <td>0.2</td>
      <td>[–0.43, 0.36]</td>
      <td>0.861</td>
    </tr>
    <tr>
      <td>(Intercept)</td>
      <td>16.53</td>
      <td>1.49</td>
      <td>[13.61, 19.44]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Own</td>
      <td>6.4</td>
      <td>0.16</td>
      <td>[6.09, 6.72]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Other</td>
      <td>–0.02<br>Note. Fixed-eﬀects coeﬃcients with all model predictors. Participants represented the</td>
      <td>0.22</td>
      <td>[–0.44, 0.41]</td>
      <td>0.94</td>
    </tr>
    <tr>
      <td>random eﬀect.</td>
      <td></td>
      <td>See supplementary section “Mixed Models” for step-wise regression</td>
      <td></td>
      <td></td>
    </tr>
    <tr><td colspan="5"><strong>results.</strong></td></tr>
  </tbody>
</table>

Note. Fixed-eﬀects coeﬃcients with all model predictors. Participants represented the random eﬀect. See supplementary section “Mixed Models” for step-wise regression results.

T

### Table 13
*Extensions: Mean domain diﬃculty and mean comparative ability estimates tested against the average (scale midpoint).*

<table>
  <thead>
    <tr>
      <th>Ability</th>
      <th>Easy domain condition<br>Domain</th>
      <th>Percentile</th>
      <th>Domain</th>
      <th>Diﬃcult domain condition<br>Percentile</th>
    </tr>
    <tr>
      <th></th>
      <th>diﬃculty</th>
      <th>estimate<br>1</th>
      <th>diﬃculty</th>
      <th>estimate<br>2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Using mouse</td>
      <td>3.13 (2.90)</td>
      <td>71.27 (20.51)∗∗∗</td>
      <td>5.77 (2.36)</td>
      <td>55.79 (21.12)∗∗∗</td>
    </tr>
    <tr>
      <td>Driving</td>
      <td>4.58 (2.77)</td>
      <td>66.32 (22.74)∗∗∗</td>
      <td>7.16 (2.15)</td>
      <td>40.63 (29.28)∗∗∗</td>
    </tr>
    <tr>
      <td>Riding bicycle</td>
      <td>3.88 (2.77)</td>
      <td>65.76 (21.92)∗∗∗</td>
      <td>7.85 (2.12)</td>
      <td>48.90 (27.54)</td>
    </tr>
    <tr>
      <td>Saving money</td>
      <td>5.31 (2.76)</td>
      <td>63.63 (25.55)∗∗∗</td>
      <td>6.32 (2.60)</td>
      <td>62.68 (25.77)∗∗∗</td>
    </tr>
    <tr>
      <td>Telling jokes</td>
      <td>4.64 (2.67)</td>
      <td>59.74 (20.55)∗∗∗</td>
      <td>7.67 (1.98)</td>
      <td>40.82 (27.03)∗∗∗</td>
    </tr>
    <tr>
      <td>Playing chess</td>
      <td>6.71 (2.56)</td>
      <td>47.81 (27.55)</td>
      <td>8.34 (1.89)</td>
      <td>41.86 (27.22)∗∗∗</td>
    </tr>
    <tr>
      <td>Juggling</td>
      <td>6.07 (2.60)</td>
      <td>46.76 (27.75)</td>
      <td>7.81 (2.00)</td>
      <td>39.67 (27.66)∗∗∗</td>
    </tr>
    <tr>
      <td>Programming</td>
      <td>7.46 (2.13)</td>
      <td>49.57 (25.71)</td>
      <td>8.15 (1.84)</td>
      <td>45.36 (26.02)∗∗</td>
    </tr>
  </tbody>
</table>

*p<.05, **p<.01, ∗∗∗p<.001.
Note: Scores are displayed with the following structure: Mean (SD). 1 Scores above 50 reﬂect an above-average eﬀect, estimates below 50 reﬂect a belowaverage eﬀect. See Table 9.2 in supplementary for test statistics and CI’s. 2 See Table 9.2 in supplementary for test statistics and CI’s.

3.3.4 Relationship between domain diﬃculty and comparative ability.
As indicated above, one-sample t-tests indicated above-average-eﬀect for the easy and below-average eﬀect for the diﬃcult condition (Table 13 for mean scores and SD’s,and Tables 9.2–9.3 in the OSF supplement for test statistics). However, the below-average-eﬀect was not expressed in the easy extension condition, and the above-average-eﬀect was not clearly expressed in the diﬃcult extension condition. Item-wise correlations between comparative ability and domain diﬃculty for each ability are provided in the OSF supplement under ‘Extension conditions: correlations between comparative ability and domain diﬃculty ratings for each ability domain’. The easy domain condition contains mixed results of medium to no associations (p <.936), whereas the diﬃcult domain condition contains negative associations for all abilities (p <.001). Congruent with original and replication ﬁndings, there were negative relationships between domain diﬃculty and comparative ability in the easy r(6) = –0.90 (p = .002, 95% CI [–0.982, –0.537]) ; and diﬃcult conditions (r(6) = -0.75, p = .033, 95% CI [–0.951, –0.092]).
See OSF supplement: equivalence tests 7–8. See OSF supplement: equivalence tests 9–10.

3.3.5 Additional analyses for the relationship between domain diﬃculty and comparative ability.

### Table 14
*Estimated ﬁxed-eﬀects coeﬃcients of the mixed-eﬀects regression model with changes in Comparative Ability explained by Others’ and Own Ability in the Extension Con- ditions.*

<table>
  <thead>
    <tr>
      <th>Predictors</th>
      <th>B<br>Comparative Ability Easy Condition</th>
      <th>S.E.</th>
      <th>CI</th>
      <th>p</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>(Intercept)</td>
      <td>18.6</td>
      <td>2.35</td>
      <td>[13.99, 23.21]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Own</td>
      <td>6.37</td>
      <td>0.18</td>
      <td>[6.02, 6.71]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Other</td>
      <td>–0.13</td>
      <td>0.21</td>
      <td>[–0.54, 0.28]</td>
      <td>0.546</td>
    </tr>
    <tr>
      <td>Diﬃculty</td>
      <td>–0.41</td>
      <td>0.16</td>
      <td>[–0.72, –0.11]</td>
      <td>0.008</td>
    </tr>
    <tr>
      <td>Desirability</td>
      <td>0.15</td>
      <td>0.21</td>
      <td>[–0.26, 0.56]</td>
      <td>0.468</td>
    </tr>
    <tr>
      <td>Ambiguity</td>
      <td>–0.1<br>Comparative Ability Diﬃcult Condition</td>
      <td>0.19</td>
      <td>[–0.47, 0.27]</td>
      <td>0.6</td>
    </tr>
    <tr>
      <td>(Intercept)</td>
      <td>25.57</td>
      <td>2.66</td>
      <td>[20.36, 30.79]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Own</td>
      <td>6.11</td>
      <td>0.17</td>
      <td>[5.78, 6.45]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Other</td>
      <td>–0.16</td>
      <td>0.22</td>
      <td>[–0.59, 0.26]</td>
      <td>0.451</td>
    </tr>
    <tr>
      <td>Diﬃculty</td>
      <td>–1.04</td>
      <td>0.2</td>
      <td>[–1.43, –0.64]</td>
      <td>&lt;0.001</td>
    </tr>
    <tr>
      <td>Desirability</td>
      <td>0.16</td>
      <td>0.2</td>
      <td>[–0.22, 0.55]</td>
      <td>0.405</td>
    </tr>
    <tr>
      <td>Ambiguity</td>
      <td>–0.17</td>
      <td>0.19</td>
      <td>[–0.55, 0.21]</td>
      <td>0.37</td>
    </tr>
  </tbody>
</table>

Note. The table presents the ﬁxed-eﬀects coeﬃcients with all the model predictors. Participants represented the random eﬀect. See supplementary section “Mixed Models” for step-wise regression results.

See OSF supplement Tables 5.1, 5.2, 7.1 and 7.2 for correlations between mean scores across inventories in the extension conditions.

3.3.6 Comparisons of ambiguity and diﬃculty ratings between the three conditions
As parametric assumptions were not met , to test whether diﬀerent domain deﬁnitions from the original domains would result in diﬀerent domain diﬃculty and ambiguity ratings, we ﬁrst conducted a Kruskal-Wallis test that showed diﬀerences in diﬃculty scores across conditions (H(2) = 237, p < .001, 2 = 0.34; Figure 1). Supporting the ﬁrst part of H3–4, post-hoc Bonferroni corrected Mann-Whitney tests showed that, compared to the replication condition (Mdnreplication = 6.00, Mreplication = 6.05, SD = 1.15), participants in the easy domain condition (Mdneasy = 5.00, Measy = 5.22, SD = 1.63) rated lower domain diﬃculty (p < .001). Participants in the diﬃcult domain condition (Mdndiﬃcult = 7.78, Mdiﬃcult = 7.39, SD = 1.19) rated higher domain diﬃculty than in the other conditions (ps < .001; Figure 1A). We conducted a second Kruskal-Wallis test and found diﬀerences in participants’ ambiguity ratings between the three conditions (H(2) = 11.47, p = .003, 2 = 0.014; Figure 1B). As predicted in the second part of H3–4, post-hoc Bonferroni corrected Mann-Whitney tests showed replication condition ambiguity ratings (Mdnreplication = 2.88 Mreplication = 3.00, SD = 1.24) to be lower than both the easy extension condition (Mdneasy = 2.38, Measy = 2.68, SD = 1.23; padj = 0.01) and the diﬃcult extension condition ambiguity ratings (Mdndiﬃcult = 2.38, Mdiﬃcult = 2.76, SD = 1.43; padj = 0.01). We found no support for diﬀerences between easy and diﬃcult extension conditions’ ambiguity ratings, (padj ≈ 1.00).
3.3.7 Relationship between comparative ability, and domain diﬃculty and desirability (examining H2 in the extension conditions)
In the following section, the easy (n = 225) and diﬃcult (n = 226) extension conditions results are analyzed in the same way as reported above for the replication condition. For the above- and below-average eﬀects across all abilities, we found a strong negative correlation between comparative ability estimates and domain diﬃculty in both extension conditions (see above). Item-wise comparative-ability–domain-diﬃculty correlations are provided in the OSF supplement ‘Extension conditions: correlations between comparative ability and domain diﬃculty ratings for each ability domain’.
When comparing desirability ratings between easy and diﬃcult ability domains via Wilcoxon signed ranks test, in the easy extension condition easy (M = 4.23, SD = 2.13) abilities to be more desirable than diﬃcult abilities (M = 6.22, SD = 1.56; Z(223) = –10.62, p < .001, r = 0.75, 95% CI [0.70, 0.80]), as well as in the diﬃcult extension condition easy abilities (M = 6.78, SD = 1.44), diﬃcult (M = 7.99, SD = 1.30; Z(224) = –9.26, p < .001, r = 0.69, 95% CI [0.62, 0.75]). One-sample Wilcoxon tests revealed that all domain-speciﬁc desirability scores were higher than the scale midpoint (ps < .001; OSF supplement Tables 9.5–9-6). Moreover, correlations between comparative ability and desirability in easy (r(6)
See “Statistical assumptions and normality Tests” section in the detailed supplementary on OSF for parametric tests.

= 0.66, p = .074, 95% CI [–0.08, 0.93]) and diﬃcult extension conditions (r(6) = 0.15, p =.72, 95% CI [–0.62, 0.77]) remain uncertain.
3.3.8 Extension H2: Additional Analyses for the relationship between comparative ability, and domain diﬃculty and desirability
Similarly, we found a negative association between comparative ability and domain diﬃculty ratings when using vector-compiled scores in the easy extension condition (r(1798) = –0.27,

95% CI [–0.31, –0.22]) as well as in the diﬃcult extension condition (r(1806) = -0.31, 95% CI [–0.35, –0.27]) . Similar to our ﬁndings for the replication condition, when using inventory mean scores, we found a positive association between comparative ability and mean domain diﬃculty ratings in the easy extension condition (r(223) = 0.32, p < .001, 95% CI [0.19, 0.43]) and a negative association in the diﬃcult extension condition (r(223) = –0.13, p = .05, 95% CI [–0.26, –0.0002]) .

### 3.4 Replication Evaluation

The following section compares the original study and current replication based on the replication evaluation criteria by LeBel et al. (2019). We found clear support for replication hypotheses H1 and H2. Both correlations between own absolute ability and comparative ability across all abilities displayed as conducted in the original study and additional analyses detected strong eﬀects in the same direction as the original, but we found no support for diﬃculty as a predictor of comparative ability in a mixed-eﬀects model using the replication data (Table 15). Positive and signiﬁcant standardized betas for all own absolute abilities, and predominantly negative and non-signiﬁcant standardized betas for others’ absolute abilities were replicated (Table 16). The strong evidence bolsters Kruger’s research on egocentrism as comparative ability judgments are based on participants’ own levels of ability instead of their perceptions of others’ level of ability (Kruger, 1999; Kruger & Burrus, 2004). An underlying mechanism might be focalism, a complementary bias on people’s tendency to place more judgmental weight on the target (self) and less weight on the referent (others)
See OSF supplement: equivalence tests 11–12. See OSF supplement: equivalence tests 13–14. See OSF supplement Table 11: equivalence tests 15–16. See OSF supplement Table 11: equivalence tests 17–18. As this analysis was an oversight in our preregistration, an additional power simulation was executed, showing excellent power for observing main and interaction eﬀects of a 3x2 mixed ANOVA. See OSF supplement “Power Simulation for Exploratory Analysis” for more information.

****

**** **** ****

**** ****

parative ability ratings by condition. Panel B. Mean comparative ability ratings by diﬃculty. Panel C. Mean easy and diﬃcult mean comparative ability ratings by condition with SD. ns p>.05, ∗p<.05,

∗∗p<.01, ∗∗∗p<.001, ∗∗∗∗p<.0001.

when making direct comparisons between the two (Krizan & Suls, 2008). An alternative explanation is that people simply have more information about themselves than they do about others. Paired with expectations about distributions of values of luck and skills, participants might have rationally judged, based on their best guess, that their own abilities are higher compared to others’ abilities when tasks were easy and vice versa when tasks were diﬃcult (Moore & Healy, 2008).
Above and below-average eﬀects (H2) replicated with a slightly smaller eﬀect. Additional analyses revealed a smaller eﬀect in the same direction, but when averaging the

Table 15: Comparison of correlational study eﬀect sizes between the original article and

replication based on criteria created by LeBel et al. (2019).

### Table 15
*Comparison of correlational study eﬀect sizes between the original article and replication based on criteria created by LeBel et al. (2019).*

<table>
  <thead>
    <tr>
      <th></th>
      <th>p</th>
      <th>Correlation<br>coeﬃcient (r)<br>and 95% CI</th>
      <th>p</th>
      <th>Correlation<br>coeﬃcient (r)<br>and 95% CI</th>
      <th></th>
    </tr>
    <tr>
      <th>Variables</th>
      <th>Original study</th>
      <th></th>
      <th>Replication</th>
      <th></th>
      <th>Replication</th>
    </tr>
    <tr>
      <th>(across all</th>
      <th></th>
      <th></th>
      <th>condition</th>
      <th></th>
      <th>evaluation</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="6"><strong>abilities)</strong></td></tr>
    <tr>
      <td>Own ability</td>
      <td>&lt;.001</td>
      <td>r(6) = .95</td>
      <td>&lt;.001</td>
      <td>r(6) = 0.99,</td>
      <td>Signal-</td>
    </tr>
    <tr>
      <td>and</td>
      <td></td>
      <td>[0.90, 0.97]</td>
      <td></td>
      <td>[0.96, 1.00]</td>
      <td>consistent</td>
    </tr>
    <tr><td colspan="6"><strong>comparative</strong></td></tr>
    <tr><td colspan="6"><strong>ability</strong></td></tr>
    <tr>
      <td>Inventory</td>
      <td>/</td>
      <td>/</td>
      <td>&lt;.001; &lt;.001</td>
      <td>r(238) = .85</td>
      <td>Additional</td>
    </tr>
    <tr>
      <td>mean and</td>
      <td></td>
      <td></td>
      <td></td>
      <td>[0.82, 0.89];</td>
      <td>analyses</td>
    </tr>
    <tr>
      <td>absolute own</td>
      <td></td>
      <td></td>
      <td></td>
      <td>r(1918) =</td>
      <td></td>
    </tr>
    <tr>
      <td>ability and</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.50, [0.46,</td>
      <td></td>
    </tr>
    <tr>
      <td>comparative</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.53]; Own (B</td>
      <td></td>
    </tr>
    <tr>
      <td>ability</td>
      <td></td>
      <td></td>
      <td></td>
      <td>= 7.18) vs<br>others’ ability<br>(B = –0.42)</td>
      <td></td>
    </tr>
    <tr>
      <td>Domain</td>
      <td>&lt;.001</td>
      <td>r(6) = –.96,</td>
      <td>0.007</td>
      <td>r(6) = –0.85,</td>
      <td>Signal-</td>
    </tr>
    <tr>
      <td>diﬃculty and</td>
      <td></td>
      <td>[–0.98, –0.92]</td>
      <td></td>
      <td>[–0.97 -0.37]</td>
      <td>consistent,</td>
    </tr>
    <tr>
      <td>comparative</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td>smaller</td>
    </tr>
    <tr><td colspan="6"><strong>ability</strong></td></tr>
    <tr>
      <td>Inventory</td>
      <td>/</td>
      <td>/</td>
      <td>.013; &lt;.001</td>
      <td>r(238) = 0.16</td>
      <td>Additional</td>
    </tr>
    <tr>
      <td>mean and</td>
      <td></td>
      <td></td>
      <td></td>
      <td>[0.04, 0.28];</td>
      <td>analyses</td>
    </tr>
    <tr>
      <td>absolute</td>
      <td></td>
      <td></td>
      <td></td>
      <td>r(1918) =</td>
      <td></td>
    </tr>
    <tr>
      <td>domain</td>
      <td></td>
      <td></td>
      <td></td>
      <td>–0.35, [–0.39,</td>
      <td></td>
    </tr>
    <tr>
      <td>diﬃculty and</td>
      <td></td>
      <td></td>
      <td></td>
      <td>–0.31];</td>
      <td></td>
    </tr>
    <tr>
      <td>comparative</td>
      <td></td>
      <td></td>
      <td></td>
      <td>Diﬃculty as</td>
      <td></td>
    </tr>
    <tr>
      <td>ability</td>
      <td></td>
      <td></td>
      <td></td>
      <td>predictor of<br>comparative</td>
      <td></td>
    </tr>
  </tbody>
</table>

-0.04

entire inventory for each participant and thereby reducing the variability in responses, a Simpson’s paradox seems to occur. Additionally, we found no support for diﬃculty as a predictor of comparative ability in a mixed regression model using the replication data, but we found support in both extensions. Participants tended to indicate higher rather than lower comparative ability in both the replication and the easy conditions, where diﬃculty ratings were normally distributed. This was not the case for the diﬃcult condition, where

Judgmental Judgmental Judgmental Judgmental weight of own weight of weight of own weight of
ability1 others’ ability1 ability1 others’ ability1

### Table 16
*Comparison of mean comparative ability estimates and judgmental weight of own versus others’ abilities by domain diﬃculty between the original study and replication condition.*

<table>
  <thead>
    <tr>
      <th>Driving</th>
      <th>.89∗∗∗∗</th>
      <th>–.25∗</th>
      <th>0.85∗∗∗</th>
      <th>–0.11∗∗</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Riding bicycle</td>
      <td>.61∗∗∗∗</td>
      <td>–0.02</td>
      <td>0.76∗∗∗</td>
      <td>–0.06</td>
    </tr>
    <tr>
      <td>Saving money</td>
      <td>.90∗∗∗∗</td>
      <td>–.25∗∗∗</td>
      <td>0.79∗∗∗</td>
      <td>–0.05</td>
    </tr>
    <tr>
      <td>Telling jokes</td>
      <td>.91∗∗∗∗</td>
      <td>–0.03</td>
      <td>0.75∗∗∗</td>
      <td>0.04</td>
    </tr>
    <tr>
      <td>Playing chess</td>
      <td>.96∗∗∗∗</td>
      <td>–.22∗∗</td>
      <td>0.82∗∗∗</td>
      <td>–0.03</td>
    </tr>
    <tr>
      <td>Juggling</td>
      <td>.89∗∗∗∗</td>
      <td>–0.16</td>
      <td>0.59∗∗∗</td>
      <td>0.18∗∗</td>
    </tr>
    <tr>
      <td>Programming</td>
      <td>.85∗∗∗∗</td>
      <td>–0.1</td>
      <td>0.83∗∗∗</td>
      <td>–0.06</td>
    </tr>
  </tbody>
</table>

Note. The original study only provided the standardized betas and p-values. The transformed R2 and F2 values would only represent the eﬀect size of one predictor instead of the overall regression, so only the p-values and directions were compared. 1Standardised betas from multiple regressions predicting participants’ comparative ability (percentile) estimates from their estimates of their own absolute ability and the absolute ability of their peers, respectively.
∗p < .05. ∗∗p < .01. ∗∗∗p < .001. ∗∗∗∗p < .0001.

diﬃculty ratings were right-skewed. In other words, the Simpson paradox was produced by the above-average-eﬀect being stronger than the below-average-eﬀect in the replication and the easy conditions. Overall, this shows the contextual eﬀects of the inventory’s diﬃculty on participants’ ratings of tasks diﬃculty and comparative ability. Using both one-sample Wilcoxon and t-tests, both above-and-below-average eﬀects replicated with smaller eﬀects, whereas above-average eﬀect sizes replicated closer to the original study (Table 17). Despite smaller eﬀect sizes, the observed results support above-and-below-average eﬀects. The prevalence of the below-average-eﬀect also demonstrates that motivated reasoning to

see oneself as superior fails to account for certain situations, such as for diﬃcult abilities in the replication.

### Table 17
*Comparison of one-sample t-test eﬀect sizes between the original article and replication based on criteria created by LeBel et al. (2019).*

<table>
  <thead>
    <tr>
      <th></th>
      <th>Cohen’s d and 95% CI</th>
      <th>Replication outcome</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="3"><strong>Original study (n=37)</strong></td></tr>
    <tr>
      <td>Each easy ability</td>
      <td>0.90 [0.22, 1.57]</td>
      <td></td>
    </tr>
    <tr>
      <td>Each diﬃcult ability</td>
      <td>–1.44 [–2.17, –0.72]</td>
      <td></td>
    </tr>
    <tr><td colspan="3"><strong>(excluding telling jokes)</strong></td></tr>
    <tr><td colspan="3"><strong>Replication condition (n=240)</strong></td></tr>
    <tr><td colspan="3"><strong>Easy abilities</strong></td></tr>
    <tr>
      <td>Using mouse</td>
      <td>1.18 [1.02, 1.35]</td>
      <td>Signal-consistent</td>
    </tr>
    <tr>
      <td>Driving</td>
      <td>0.69 [0.55, 0.83]</td>
      <td>Signal-consistent, smaller</td>
    </tr>
    <tr>
      <td>Riding bicycle</td>
      <td>0.54 [0.40, 0.67]</td>
      <td>Signal-consistent, smaller</td>
    </tr>
    <tr>
      <td>Saving money</td>
      <td>0.61 [0.47, 0.75]</td>
      <td>Signal-consistent, smaller</td>
    </tr>
    <tr><td colspan="3"><strong>Diﬃcult abilities</strong></td></tr>
    <tr>
      <td>Telling jokes</td>
      <td>0.11 [–0.02, 0.23]</td>
      <td>No signal</td>
    </tr>
    <tr>
      <td>Playing chess</td>
      <td>–0.33 [–0.46, –0.20]</td>
      <td>Signal-consistent, smaller</td>
    </tr>
    <tr>
      <td>Juggling</td>
      <td>–0.65 [–0.79, –0.51]</td>
      <td>Signal-consistent, smaller</td>
    </tr>
    <tr>
      <td>Programming</td>
      <td>–0.32 [–0.45, –0.19]</td>
      <td>Signal-consistent, smaller</td>
    </tr>
  </tbody>
</table>

## Discussion

We replicated and extended the ﬁndings in Kruger’s (1999) Study 1. Both the replication and the extension results provide strong support for above- and below-average eﬀects, depending on diﬃculty. In addition, we present important boundary conditions. First, above-and-below-average eﬀects appear stronger the more diﬃcult the domain abilities are (compare Tables 8 and 11). Second, the diﬃculty of diﬀerent activities (ability domains) might provoke or suppress below -or above-average-eﬀects; we observed a below-averageeﬀect when the presented abilities were diﬃcult, and vice versa, an above-average eﬀect when the presented abilities were easy. In that context, we observed an interaction eﬀect between manipulations (making the original scale easier or more diﬃcult) and item-group diﬃculty (easy vs diﬃcult items), looking at comparative ability. Ambiguity was low across conditions with additional information introduced in the extensions decreasing ambiguity.

### 4.1 Replication outcomes

Egocentrism is a compelling, yet only one of many explanations for above-and-belowaverage-eﬀects (Zell et al., 2020). Alternatively, judgments might be rationally based on diﬀerential access to information inﬂuencing predictions (Moore & Small, 2007). In other words, by having more information about the own than others’ performance in diﬀerent activities, others’ performance is evaluated less extremely than the own performance (Moore & Healy, 2008).
Moreover, the replication advances our understanding of the conditions in which the above or below-average eﬀects are more pronounced, i.e., when abilities’ diﬃculty and supplied information about them diﬀer. It complements a recent meta-analysis on the above-average-eﬀect (Zell et al., 2020), showing a larger eﬀect when using the direct (compare oneself to others on a single scale with the midpoint deﬁned as average) rather than indirect testing method (assess oneself and the comparison group independent from each other, with the average being deﬁned as the diﬀerence between the two values). Fewer research center on the below-average-eﬀect, yet success in replicating the eﬀect suggest that the same conditions may also be applicable in strengthening the below-average eﬀect.
On the other hand, the replication’s smaller eﬀect sizes challenge the inﬂuence of certain established factors on the eﬀects. For instance, people showed the strongest biases in comparative ability judgments when the comparison group was abstract instead of concrete, and no speciﬁc information and contact with the comparison group contributes to that abstractness (Alicke et al., 1995).
A notable discrepancy between the original and replication is the comparison group: original study participants compared themselves to other students from their psychology course, which was much more concrete than replication participants comparing themselves to others of the same age, gender, and socioeconomic background. The replication’s smaller eﬀects suggest that in contrast to past explanations, people may not display tendencies to choose vulnerable comparison targets to compare themselves with when given an abstract referent group (Chambers & Windschitl, 2004). As people display preferences in selecting representative targets, they might choose comparison targets of varying ability depending on task diﬃculty, and the availability of information and cognitive resources (Nisbett et al., 1983). This may have been the case for the current replication and is a promising direction for future research.
4.2 Outcomes of the extensions to the original study
Both H3 and H4 were supported. We found lower domain diﬃculty ratings in the easy domain condition than the replication condition (d = 0.59) and higher domain diﬃculty ratings in the diﬃcult domain condition than the replication condition (d = 1.15) supporting the ﬁrst part of the extension hypotheses (H3–4) on diﬀerences in domain diﬃculty. As interpretations of easy or diﬃcult abilities contribute to diﬀerent perceptions of domain

diﬃculty, the observed results provide insight on how this aﬀects participant interpretation of “average” ability. In a study by Kim et al. (2017), people construed below-median averages and showed above-average eﬀects for abilities perceived as easy, and construed averages at or above the median for abilities perceived as diﬃcult. For accurate assessments of comparative ability judgments, researchers not only need to ascertain how people interpret “average” ability, but also place eﬀorts in lowering variations in the perceived diﬃculty of abilities. Hence, the original domain deﬁnitions may have been open to interpretation, inﬂuencing the results.
Moreover, we found support for the second part of H3–4, that ambiguity was lower in the replication conditions. Eventually, more information provided might have led to clariﬁcation and hence decreased perceptions of ambiguity. Previous research showed a tendency to view oneself as above-average for ambiguous abilities (Dunning et al., 1989), and to select favorable, self-serving deﬁnitions amongst ambiguous traits describing a wide variety of behaviors (Gilovich, 1983; Kunda, 1987), which could not be reﬂected from our data. Finally, comparing comparative ability scores across conditions (replication vs extensions) and by the diﬃculty of the items (easy vs diﬃcult), show an interaction eﬀect. That indicates that both domain diﬃculty and ambiguity might inﬂuence comparative ability ratings and thereby above-and-below-average-eﬀects. However, despite the presented extensions potentially presenting the inﬂuence of abilities’ diﬃculty and their deﬁnitions’ ambiguity on the eﬀects, more research is needed to address above-and-below-averageeﬀects’ boundary conditions.

### 4.3 Limitations and future directions

Deviating from the original study, in our replication we measured the continuous relationship between variables and analyzed data on participant and item levels. Moreover, possible inferences from comparisons between added and original study correlations between domain diﬃculty and comparative ability are limited. Our tests supported original ability categorizations as easy or diﬃcult, all original study tests (including one-sample tests and correlations of ratings across all abilities) were also carried out for the replication condition. While we recommend future replications testing the continuous relationship between variables to avoid limitations in performing study comparisons, misclassiﬁcation, and issues in categorizing continuous variables, we also caution of low reliability when using the presented scale and particularly the suggested (easy and diﬃcult ability) subscales (Table 5).
Furthermore, the replication’s ability domain deﬁnitions are all based on Kruger’s (1999) original domains. Yet, these domains may not be as accurate and widely applicable at present. For example, a recent survey indicated that the easy ability “saving money” is challenging for the majority, with 69% of Americans having less than $1000 in their savings accounts (Huddleston, 2019). For future tests, the current ability domains can be updated and pretested. Although Kim et al. (2017) found the above-average-eﬀect, most of

the 14-items they used were general abilities such as written or spoken expression. More relevant and comprehensive items can be included in future studies and bigger pretest samples (original study: n = 39) used to select ability domains and validate the instrument.
How do people assess task diﬃculty? This question goes beyond the scope of the current investigation yet is a critical open question if diﬃculty serves as a moderator between the above and below average eﬀects. Diﬃculty has been described in previous research to increase as a function of cognitive and/or physical load, with those loads being rather additive than interactive components in making diﬃculty (Feghhi & Rosenbaum, 2019). Diﬀerent factors might be linked to such perceptions, such as error probability, weights of errors (one error is worse than another), attention demands or potentially a cost-beneﬁt calculation determining judgments of diﬃculty (Feghhi & Rosenbaum, 2019).
The underlying mechanisms of task diﬃculty judgments remain unclear, yet in our extension’s stimuli, we attempted to embed quantitative numerical information regarding load constructed to be perceived as more and less diﬃcult. We found that these were indeed rated as more and less diﬃcult by the participants. This allows for the use of a quantiﬁable latent concept such as load as a predictor of diﬃculty. The operationalization of such latent concepts requires systematic testing in future research.
Together with many past studies, the present replication only establishes the ubiquity of the above and below-average eﬀects. Much less is known about the eﬀects’ impacts, especially for the below-average eﬀect. The directionality of the above-average eﬀect’s impacts is still debated. Tendencies to see oneself as better than others can serve a wide variety of aﬀective, cognitive, and social functions such as temporary boosts in task performance, longer life expectancy, and well-being (Bopp et al., 2012; Ehrlinger & Dunning, 2003; Taylor & Brown, 1988; Zell et al., 2020). But it can also result in harmful long-term consequences of having unrealistic expectations, heightened disengagement, and decreased self-esteem (Polivy & Herman, 2000; Robins & Beer, 2001). In contrast, less research has been conducted on the below-average-eﬀect’s impacts, predominantly focusing on its negative consequences, such as lower grades (Mattern et al., 2010), or worse subjective well-being (Goetz et al., 2006). Other research suggested that the below-average-eﬀect can also induce positive motivational and behavioral consequences in the long run (Whillans et al., 2020). This highlights the need for continued research on the below-and-above-averageeﬀects’ consequences.

## Conclusion

We closely replicated Kruger’s (1999) study, showing the above- and below-average eﬀects to be robust. Manipulating the diﬃculty of (easy and diﬃcult) ability domains participants were to compare themselves with others, which showed that easier items might provoke the above-average eﬀect but dampen the below-average eﬀect and vice versa for more diﬃcult items.

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## Tables (unlocated in body)

### Table 5
*Classiﬁcation of the replication, based on LeBel et al. (2018).*

```
Judgment and Decision Making, Vol. 17, No. 1, January 2022 Above and below average
Table 5: Classiﬁcation of the replication, based on LeBel et al. (2018).
Design facet Replication Details of deviation
IV opera- Same
tionalization
DV opera- Same
tionalization
IV1 ability domains is changed from one condition of 4 easy IV stimuli Similar, with
and 4 diﬃcult abilities, to 3 conditions of the replication an added
group, the easy domain group, and the diﬃcult domain extension
group. Participants were presented with either the original
```

