Daniël Lakens
Dados Biográficos
| ID | 1833158 |
|---|---|
| NOME | Daniël Lakens |
| PRENOMES | Daniël |
| SOBRENOME | Lakens |
| ASSINATURA | LAKENS D |
| AFILIAÇÕES | Eindhoven University of Technology |
| ORCID | 0000-0002-0247-239X |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 36 |
| TOTAL DE CITAÇÕES | 268 |
| TOTAL COMO AUTOR | 36 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2009 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 8 |
Rethinking Type S and Type M Errors
Gelman and Carlin introduced Type S (sign) and Type M (magnitude) errors to highlight the possibility that statistically significant results in published articles are misleading. Although these concepts have been proposed to be useful both when designing a study (prospective) and when evaluating results (retroactive), we argue that these statistics do not facilitate the proper design of studies or the meaningful interpretation of results. Type S …
A systemic approach to better coordination in science
Finding joy, creativity and meaning through unusual interdisciplinary collaborations
Academics are increasingly calling for and asked to, work in interdisciplinary teams to address pressing social-ecological challenges. However, there are significant barriers to pursuing interdisciplinary collaborations within current university structures. Taking the first two years of our Centre for Unusual Collaborations (CUCo) as a case study of setting up a space for exploration and experimentation, we discuss how unusual interdisciplinary c…
Shifting the Level of Selection in Science
Criteria for recognizing and rewarding scientists primarily focus on individual contributions. This creates a conflict between what is best for scientists’ careers and what is best for science. In this article, we show how the theory of multilevel selection provides conceptual tools for modifying incentives to better align individual and collective interests. A core principle is the need to account for indirect effects by shifting the level at wh…
Make abandoned research publicly available
Are Small Effects the Indispensable Foundation for a Cumulative Psychological Science? A Reply to Götz et al. (2022)
In the January 2022 issue of Perspectives, Götz et al. argued that small effects are “the indispensable foundation for a cumulative psychological science.” They supported their argument by claiming that (a) psychology, like genetics, consists of complex phenomena explained by additive small effects; (b) psychological-research culture rewards large effects, which means small effects are being ignored; and (c) small effects become meaningful at sca…
Not All Effects Are Indispensable
To help move researchers away from heuristically dismissing “small” effects as unimportant, recent articles have revisited arguments to defend why seemingly small effect sizes in psychological science matter. One argument is based on the idea that an observed effect size may increase in impact when generalized to a new context because of processes of accumulation over time or application to large populations. However, the field is now in danger o…
The epistemic and pragmatic function of dichotomous claims based on statistical hypothesis tests
Researchers commonly make dichotomous claims based on continuous test statistics. Many have branded the practice as a misuse of statistics and criticize scientists for the widespread application of hypothesis tests to tentatively reject a hypothesis (or not) depending on whether a p-value is below or above an alpha level. Although dichotomous claims are rarely explicitly defended, we argue they play an important epistemological and pragmatic role…
Sample Size Justification
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable information given the inferential goals of the researcher. In this overview article six approaches are discussed to justify the sample size in a quantitative empirical study: 1) collecting data from (almost) the ent…
A pre-registered, multi-lab non-replication of the action-sentence compatibility effect (ACE)
The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward ) matches the direction of the action in the to-be-judged sentence (e.g., Art gave …
Simulation-Based Power Analysis for Factorial Analysis of Variance Designs
Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analys…
Why Hypothesis Testers Should Spend Less Time Testing Hypotheses
For almost half a century, Paul Meehl educated psychologists about how the mindless use of null-hypothesis significance tests made research on theories in the social sciences basically uninterpretable. In response to the replication crisis, reforms in psychology have focused on formalizing procedures for testing hypotheses. These reforms were necessary and influential. However, as an unexpected consequence, psychological scientists have begun to …
The Practical Alternative to the p Value Is the Correctly Used p Value
Because of the strong overreliance on p values in the scientific literature, some researchers have argued that we need to move beyond p values and embrace practical alternatives. When proposing alternatives to p values statisticians often commit the “statistician’s fallacy,” whereby they declare which statistic researchers really “want to know.” Instead of telling researchers what they want to know, statisticians should teach researchers which qu…
Using anchor-based methods to determine the smallest effect size of interest
Effect sizes are an important outcome of quantitative research, but few guidelines exist that explain how researchers can determine which effect sizes are meaningful. Psychologists often want to study effects that are large enough to make a difference to people's subjective experience. Thus, subjective experience is one way to gauge the meaningfulness of an effect. We propose and illustrate one method for how to quantify the smallest subjectively…
Equivalence Testing for Psychological Research
Psychologists must be able to test both for the presence of an effect and for the absence of an effect. In addition to testing against zero, researchers can use the two one-sided tests (TOST) procedure to test for equivalence and reject the presence of a smallest effect size of interest (SESOI). The TOST procedure can be used to determine if an observed effect is surprisingly small, given that a true effect at least as extreme as the SESOI exists…
Many Labs 2
We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p < .05), we found that 15 (54%) of …
Improving Inferences About Null Effects With Bayes Factors and Equivalence Tests
Researchers often conclude an effect is absent when a null-hypothesis significance test yields a nonsignificant p value. However, it is neither logically nor statistically correct to conclude an effect is absent when a hypothesis test is not significant. We present two methods to evaluate the presence or absence of effects: Equivalence testing (based on frequentist statistics) and Bayes factors (based on Bayesian statistics). In four examples fro…
Two experience sampling studies examining the variation of self-control capacity and its relationship with core affect in daily life
Justify your alpha
Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test
When comparing two independent groups, psychology researchers commonly use Student’s t-tests. Assumptions of normality and homogeneity of variance underlie this test. More often than not, when these conditions are not met, Student’s t-test can be severely biased and lead to invalid statistical inferences. Moreover, we argue that the assumption of equal variances will seldom hold in psychological research, and choosing between Student’s t-test and…
Too True to be Bad
Psychology journals rarely publish nonsignificant results. At the same time, it is often very unlikely (or “too good to be true”) that a set of studies yields exclusively significant results. Here, we use likelihood ratios to explain when sets of studies that contain a mix of significant and nonsignificant results are likely to be true or “too true to be bad.” As we show, mixed results are not only likely to be observed in lines of research but a…
Equivalence Tests
Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effec…
When power analyses based on pilot data are biased
When designing a study, the planned sample size is often based on power analyses. One way to choose an effect size for power analyses is by relying on pilot data. A-priori power analyses are only accurate when the effect size estimate is accurate. In this paper we highlight two sources of bias when performing a-priori power analyses for between-subject designs based on pilot data. First, we examine how the choice of the effect size index (η2, ω2 …
On the reproducibility of meta-analyses
The present article highlights the need to improve the reproducibility of meta-analyses to facilitate the identification of errors, allow researchers to examine the impact of subjective choices such as inclusion criteria, and update the meta-analysis after several years. Reproducibility can be improved by applying standardized reporting guidelines and sharing all meta-analytic data underlying the meta-analysis, including quotes from articles to s…
A short history of the weight-importance effect and a recommendation for pre-testing
Many Labs 2
We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p < .05), we found that 15 (54%) of …
Registered Reports
Ignoring replications and negative results is bad for science. This special issue presents a novel publishing format - Registered Reports - as a partial solution. Peer review occurs prior to data collection, design and analysis plans are preregistered, and results are reported regardless of outcome. Fourteen Registered Reports of replications of important published results in social psychology are reported with strong confirmatory tests. Further,…
Performing high‐powered studies efficiently with sequential analyses
Running studies with high statistical power, while effect size estimates in psychology are often inaccurate, leads to a practical challenge when designing an experiment. This challenge can be addressed by performing sequential analyses while the data collection is still in progress. At an interim analysis, data collection can be stopped whenever the results are convincing enough to conclude that an effect is present, more data can be collected, o…
When power analyses based on pilot data are biased
When designing a study, the planned sample size is often based on power analyses. One way to choose an effect size for power analyses is by relying on pilot data. A-priori power analyses are only accurate when the effect size estimate is accurate. In this paper we highlight two sources of bias when performing a-priori power analyses for between-subject designs based on pilot data. First, we examine how the choice of the effect size index (η2, ω2 …
Justify your alpha
Improving Inferences About Null Effects With Bayes Factors and Equivalence Tests
Researchers often conclude an effect is absent when a null-hypothesis significance test yields a nonsignificant p value. However, it is neither logically nor statistically correct to conclude an effect is absent when a hypothesis test is not significant. We present two methods to evaluate the presence or absence of effects: Equivalence testing (based on frequentist statistics) and Bayes factors (based on Bayesian statistics). In four examples fro…
Movement synchrony and perceived entitativity
Using anchor-based methods to determine the smallest effect size of interest
Effect sizes are an important outcome of quantitative research, but few guidelines exist that explain how researchers can determine which effect sizes are meaningful. Psychologists often want to study effects that are large enough to make a difference to people's subjective experience. Thus, subjective experience is one way to gauge the meaningfulness of an effect. We propose and illustrate one method for how to quantify the smallest subjectively…
Why Your Highness Needs the People
Earlier research ( Schubert, 2005 ) showed that power is represented in vertical space: powerful = up and powerless = down. We propose that power is not simply structured in space in absolute terms, but that relational differences in power moderate the vertical representation of the powerful above the powerless. Two studies reveal that, when power differences are present (vs. absent), the vertical representation of power increases reliably. Power…
Two experience sampling studies examining the variation of self-control capacity and its relationship with core affect in daily life
Weight as an Embodiment of Importance
Four studies show that the abstract concept of importance is grounded in bodily experiences of weight. Participants provided judgments of importance while they held either a heavy or a light clipboard. Holding a heavy clipboard increased judgments of monetary value (Study 1) and made participants consider fair decision-making procedures to be more important (Study 2). It also caused more elaborate thinking, as indicated by higher consistency betw…
At face value
The present research demonstrates that the attention bias to angry faces is modulated by how people categorize these faces. Since facial expressions contain psychologically meaningful information for social categorizations (i.e., gender, personality) but not for non-social categorizations (i.e., eye-color), angry facial expression should especially capture attention during social categorization tasks. Indeed, in three studies, participants were s…
Movement synchrony and perceived entitativity
Weighty Matters
Previous work showed that concrete experiences of weight influence people’s judgments of how important certain issues are. In line with an embodied simulation account but contrary to a metaphor-enriched perspective, this work shows that perceived importance of an object influences perceptions of weight. Two studies manipulated information about a book’s importance, after which, participants estimated its weight. Importance information caused part…
The sound of time
Orange as a perceptual representation of the Dutch nation
Although it is generally accepted that colors carry meaning, experimental research about individual, situational, and cultural differences in the meaning of colors is scarce. The current research examines whether the Dutch national color functions as a perceptual representation of The Netherlands. A person dressed in orange clothing was judged to identify more with his nation compared with the same person dressed in blue (Study 1). When national …
Why Your Highness Needs the People
Earlier research ( Schubert, 2005 ) showed that power is represented in vertical space: powerful = up and powerless = down. We propose that power is not simply structured in space in absolute terms, but that relational differences in power moderate the vertical representation of the powerful above the powerless. Two studies reveal that, when power differences are present (vs. absent), the vertical representation of power increases reliably. Power…
Calculating and reporting effect sizes to facilitate cumulative science
Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to c…
Sailing From the Seas of Chaos Into the Corridor of Stability
Recent events have led psychologists to acknowledge that the inherent uncertainty encapsulated in an inductive science is amplified by problematic research practices. In this article, we provide a practical introduction to recently developed statistical tools that can be used to deal with these uncertainties when performing and evaluating research. In Part 1, we discuss the importance of accurate and stable effect size estimates as well as how to…
Performing high‐powered studies efficiently with sequential analyses
Running studies with high statistical power, while effect size estimates in psychology are often inaccurate, leads to a practical challenge when designing an experiment. This challenge can be addressed by performing sequential analyses while the data collection is still in progress. At an interim analysis, data collection can be stopped whenever the results are convincing enough to conclude that an effect is present, more data can be collected, o…
Registered Reports
Ignoring replications and negative results is bad for science. This special issue presents a novel publishing format - Registered Reports - as a partial solution. Peer review occurs prior to data collection, design and analysis plans are preregistered, and results are reported regardless of outcome. Fourteen Registered Reports of replications of important published results in social psychology are reported with strong confirmatory tests. Further,…
On the reproducibility of meta-analyses
The present article highlights the need to improve the reproducibility of meta-analyses to facilitate the identification of errors, allow researchers to examine the impact of subjective choices such as inclusion criteria, and update the meta-analysis after several years. Reproducibility can be improved by applying standardized reporting guidelines and sharing all meta-analytic data underlying the meta-analysis, including quotes from articles to s…
A short history of the weight-importance effect and a recommendation for pre-testing
Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test
When comparing two independent groups, psychology researchers commonly use Student’s t-tests. Assumptions of normality and homogeneity of variance underlie this test. More often than not, when these conditions are not met, Student’s t-test can be severely biased and lead to invalid statistical inferences. Moreover, we argue that the assumption of equal variances will seldom hold in psychological research, and choosing between Student’s t-test and…
Too True to be Bad
Psychology journals rarely publish nonsignificant results. At the same time, it is often very unlikely (or “too good to be true”) that a set of studies yields exclusively significant results. Here, we use likelihood ratios to explain when sets of studies that contain a mix of significant and nonsignificant results are likely to be true or “too true to be bad.” As we show, mixed results are not only likely to be observed in lines of research but a…
Equivalence Tests
Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effec…
When power analyses based on pilot data are biased
When designing a study, the planned sample size is often based on power analyses. One way to choose an effect size for power analyses is by relying on pilot data. A-priori power analyses are only accurate when the effect size estimate is accurate. In this paper we highlight two sources of bias when performing a-priori power analyses for between-subject designs based on pilot data. First, we examine how the choice of the effect size index (η2, ω2 …
Equivalence Testing for Psychological Research
Psychologists must be able to test both for the presence of an effect and for the absence of an effect. In addition to testing against zero, researchers can use the two one-sided tests (TOST) procedure to test for equivalence and reject the presence of a smallest effect size of interest (SESOI). The TOST procedure can be used to determine if an observed effect is surprisingly small, given that a true effect at least as extreme as the SESOI exists…
Many Labs 2
We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p < .05), we found that 15 (54%) of …
Improving Inferences About Null Effects With Bayes Factors and Equivalence Tests
Researchers often conclude an effect is absent when a null-hypothesis significance test yields a nonsignificant p value. However, it is neither logically nor statistically correct to conclude an effect is absent when a hypothesis test is not significant. We present two methods to evaluate the presence or absence of effects: Equivalence testing (based on frequentist statistics) and Bayes factors (based on Bayesian statistics). In four examples fro…
Two experience sampling studies examining the variation of self-control capacity and its relationship with core affect in daily life
Justify your alpha
Simulation-Based Power Analysis for Factorial Analysis of Variance Designs
Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analys…
Why Hypothesis Testers Should Spend Less Time Testing Hypotheses
For almost half a century, Paul Meehl educated psychologists about how the mindless use of null-hypothesis significance tests made research on theories in the social sciences basically uninterpretable. In response to the replication crisis, reforms in psychology have focused on formalizing procedures for testing hypotheses. These reforms were necessary and influential. However, as an unexpected consequence, psychological scientists have begun to …
The Practical Alternative to the p Value Is the Correctly Used p Value
Because of the strong overreliance on p values in the scientific literature, some researchers have argued that we need to move beyond p values and embrace practical alternatives. When proposing alternatives to p values statisticians often commit the “statistician’s fallacy,” whereby they declare which statistic researchers really “want to know.” Instead of telling researchers what they want to know, statisticians should teach researchers which qu…
Psychology (30 obras) · Computer Science (19 obras) · Mathematics (16 obras) · Statistics (14 obras) · Social Psychology (13 obras) · Cognitive psychology (10 obras) · Meta-analysis and systematic reviews (10 obras) · Econometrics (9 obras) · Epistemology (9 obras) · Mental Health Research Topics (9 obras)