Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences.

Schönbrodt, Felix D.; Wagenmakers, Eric‐Jan; Zehetleitner, Michael; Perugini, Marco

doi:10.1037/met0000061

Cited by 439 publications

(488 citation statements)

References 108 publications

(179 reference statements)

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“…A BF represents the ratio of the probability of the data given a null model to the probability of the data given an alternative model and thereby offers a gradual quantification of evidence (Schönbrodt, Wagenmakers, Zehetleitner, & Perugini, 2015). A BF10 of 3, for example, states that the data is 3 times more likely in the alternative model than in the null model.…”

Section: Discussionmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

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Section: Discussionmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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“…For a detailed description on how conducting sequential tests with Bayesian statistics and a discussion of the advantages and disadvantages of Bayesian sequential testing, see Schönbrodt, Wagenmakers, Zehetleitner, and Perugini (2015).…”

Section: Discussionmentioning

confidence: 99%

Is intermediately inspecting statistical data necessarily a bad research practice?

Lang¹

2017

TQMP

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Intermediately inspecting the statistical data of a running experiment is justifiably referred to as a bad research practice. With only a few intermediate inspections, Type I error rates inflate to a multiple of the previously defined critical alpha. On the other hand, there are research areas where intermediately inspecting data is extremely desirable if not even necessary. For this reason, in medical research, mathematical methods are known as "group-sequential testing" which compensate Type I error cumulation by adjusting critical alpha. In the field of psychological research, these methods are widely unknown or at least used very rarely. One reason may be that group-sequential tests focus on test statistics based on the normal distribution, mainly the t-test, while in psychological research often more complex experimental designs are used. The computer program APriot has been developed to enable the user to conduct Monte-Carlo simulations of what happens when intermediately inspecting the data of an ANOVA. The simulations show clearly how bad a research practice intermediately inspecting data (without adjusting alpha) is. Further, it is shown that in many cases adjusted values of alpha can be found by simulations such that the ANOVA can be used together with group-sequential testing similarly as the t-test. A last set of demonstrations shows how the power and the required number of participants of a groupsequential test can be estimated and that group-sequential testing can be favorable from an economic point of view.

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“…Third, Bayes factors allow one to accumulate data until enough evidence has been acquired for one of the competing hypotheses, compared to the other one (Cornfield, 1966; Deng, Lu, & Chen, 2016; Schönbrodt, Wagenmakers, Zehetleitner, & Perugini, in press). Indeed, in Bayesian analysis, more evidence usually increases support for one of the competing hypotheses, and not necessarily to a change in the direction of the results (see Schönbrodt et al, in press).…”

Section: Bayesian Hypothesis Testing For Threat Conditioning Datamentioning

confidence: 99%

“…Indeed, in Bayesian analysis, more evidence usually increases support for one of the competing hypotheses, and not necessarily to a change in the direction of the results (see Schönbrodt et al, in press). …”

Section: Bayesian Hypothesis Testing For Threat Conditioning Datamentioning

confidence: 99%

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

Krypotos

Klugkist

Engelhard

2017

European Journal of Psychotraumatology

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Threat conditioning procedures have allowed the experimental investigation of the pathogenesis of Post-Traumatic Stress Disorder. The ﬁndings of these procedures have also provided stable foundations for the development of relevant intervention programs (e.g. exposure therapy). Statistical inference of threat conditioning procedures is commonly based on p-values and Null Hypothesis Signiﬁcance Testing (NHST). Nowadays, however, there is a growing concern about this statistical approach, as many scientists point to the various limitations of p-values and NHST. As an alternative, the use of Bayes factors and Bayesian hypothesis testing has been suggested. In this article, we apply this statistical approach to threat conditioning data. In order to enable the easy computation of Bayes factors for threat conditioning data we present a new R package named condir, which can be used either via the R console or via a Shiny application. This article provides both a non-technical introduction to Bayesian analysis for researchers using the threat conditioning paradigm, and the necessary tools for computing Bayes factors easily.

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Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences.

Cited by 439 publications

References 108 publications

Introduction matters: Manipulating trust in automation and reliance in automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

Is intermediately inspecting statistical data necessarily a bad research practice?

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

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