scholarly journals Bayesian prediction intervals for assessing P-value variability in prospective replication studies

2017 ◽  
Vol 7 (12) ◽  
Author(s):  
Olga Vsevolozhskaya ◽  
Gabriel Ruiz ◽  
Dmitri Zaykin
Author(s):  
Mami T. Wentworth ◽  
Ralph C. Smith

In this paper, we employ adaptive Metropolis algorithms to construct densities for parameters and quantities of interest for models arising in the analysis of smart material structures. In the first step of the construction, MCMC algorithms are used to quantify the uncertainty in parameters due to measurement errors. We then combine uncertainties from the input parameters and measurement errors, and construct prediction intervals for the quantity of interest by propagating uncertainties through the models.


2018 ◽  
Vol 1 (2) ◽  
pp. 198-218 ◽  
Author(s):  
Gerd Gigerenzer

The “replication crisis” has been attributed to misguided external incentives gamed by researchers (the strategic-game hypothesis). Here, I want to draw attention to a complementary internal factor, namely, researchers’ widespread faith in a statistical ritual and associated delusions (the statistical-ritual hypothesis). The “null ritual,” unknown in statistics proper, eliminates judgment precisely at points where statistical theories demand it. The crucial delusion is that the p value specifies the probability of a successful replication (i.e., 1 – p), which makes replication studies appear to be superfluous. A review of studies with 839 academic psychologists and 991 students shows that the replication delusion existed among 20% of the faculty teaching statistics in psychology, 39% of the professors and lecturers, and 66% of the students. Two further beliefs, the illusion of certainty (e.g., that statistical significance proves that an effect exists) and Bayesian wishful thinking (e.g., that the probability of the alternative hypothesis being true is 1 – p), also make successful replication appear to be certain or almost certain, respectively. In every study reviewed, the majority of researchers (56%–97%) exhibited one or more of these delusions. Psychology departments need to begin teaching statistical thinking, not rituals, and journal editors should no longer accept manuscripts that report results as “significant” or “not significant.”


2016 ◽  
Vol 31 (1) ◽  
Author(s):  
Mohammed S. Kotb

AbstractWe suggest a ranked set sample method to improve Bayesian prediction intervals. The paper deals with the Bayesian prediction intervals in the context of an ordered ranked set sample from a certain class of exponential-type distributions. A proper general prior density function is used and the predictive cumulative function is obtained in the two-sample case. The special case of linear exponential distributed observations is considered and completed with numerical results.


2020 ◽  
Author(s):  
Yuta Hamaguchi ◽  
Hisashi Noma ◽  
Kengo Nagashima ◽  
Tomohide Yamada ◽  
Toshi A. Furukawa

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