Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events

In 2011, one of the authors (DJB) published a report of nine experiments in the Journal of Personality and Social Psychology purporting to demonstrate that an individual\u2019s cognitive and affective responses can be influenced by randomly selected stimulus events that do not occur until after his or her responses have already been made and recorded, a generalized variant of the phenomenon traditionally denoted by the term precognition. To encourage replications, all materials needed to conduct them were made available on request. We here report a meta-analysis of 90 experiments from 33 laboratories in 14 countries which yielded an overall effect greater than 6 sigma, z = 6.40, p = 1.2 7 10 with an effect size (Hedges\u2019 g) of 0.09. A Bayesian analysis yielded a Bayes Factor of 5.1 7 10 , greatly exceeding the criterion value of 100 for \u201cdecisive evidence\u201d in support of the experimental hypothesis. When DJB\u2019s original experiments are excluded, the combined effect size for replications by independent investigators is 0.06, z = 4.16, p = 1.1 7 10 , and the BF value is 3,853, again exceeding the criterion for \u201cdecisive evidence.\u201d The number of potentially unretrieved experiments required to reduce the overall effect size of the complete database to a trivial value of 0.01 is 544, and seven of eight additional statistical tests support the conclusion that the database is not significantly compromised by either selection bias or by intense \u201cp -hacking\u201d\u2014the selective suppression of findings or analyses that failed to yield statistical significance. P-curve analysis, a recently introduced statistical technique, estimates the true effect size of the experiments to be 0.20 for the complete database and 0.24 for the independent replications, virtually identical to the effect size of DJB\u2019s original experiments (0.22) and the closely related \u201cpresentiment\u201d experiments (0.21). We discuss the controversial status of precognition and other anomalous effects collectively known as psi

A systematic review on regression test selection techniques

Regression testing is verifying that previously functioning software remains after a change. With the goal of finding a basis for further research in a joint industry-academia research project, we conducted a systematic review of empirical evaluations of regression test selection techniques. We identified 27 papers reporting 36 empirical studies, 21 experiments and 15 case studies. In total 28 techniques for regression test selection are evaluated. We present a qualitative analysis of the findings, an overview of techniques for regression test selection and related empirical evidence. No technique was found clearly superior since the results depend on many varying factors. We identified a need for empirical studies where concepts are evaluated rather than small variations in technical implementations

Evaluating prediction systems in software project estimation

This is the Pre-print version of the Article - Copyright @ 2012 ElsevierContext: Software engineering has a problem in that when we empirically evaluate competing prediction systems we obtain conflicting results. Objective: To reduce the inconsistency amongst validation study results and provide a more formal foundation to interpret results with a particular focus on continuous prediction systems. Method: A new framework is proposed for evaluating competing prediction systems based upon (1) an unbiased statistic, Standardised Accuracy, (2) testing the result likelihood relative to the baseline technique of random ‘predictions’, that is guessing, and (3) calculation of effect sizes. Results: Previously published empirical evaluations of prediction systems are re-examined and the original conclusions shown to be unsafe. Additionally, even the strongest results are shown to have no more than a medium effect size relative to random guessing. Conclusions: Biased accuracy statistics such as MMRE are deprecated. By contrast this new empirical validation framework leads to meaningful results. Such steps will assist in performing future meta-analyses and in providing more robust and usable recommendations to practitioners.Martin Shepperd was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/H050329

Comparing software prediction techniques using simulation

The need for accurate software prediction systems increases as software becomes much larger and more complex. We believe that the underlying characteristics: size, number of features, type of distribution, etc., of the data set influence the choice of the prediction system to be used. For this reason, we would like to control the characteristics of such data sets in order to systematically explore the relationship between accuracy, choice of prediction system, and data set characteristic. It would also be useful to have a large validation data set. Our solution is to simulate data allowing both control and the possibility of large (1000) validation cases. The authors compare four prediction techniques: regression, rule induction, nearest neighbor (a form of case-based reasoning), and neural nets. The results suggest that there are significant differences depending upon the characteristics of the data set. Consequently, researchers should consider prediction context when evaluating competing prediction systems. We observed that the more "messy" the data and the more complex the relationship with the dependent variable, the more variability in the results. In the more complex cases, we observed significantly different results depending upon the particular training set that has been sampled from the underlying data set. However, our most important result is that it is more fruitful to ask which is the best prediction system in a particular context rather than which is the "best" prediction system