Peter Szecsi

Quantifying Support for the Null Hypothesis in Psychology: An Empirical Investigation

In the traditional statistical framework, nonsignificant results leave researchers in a state of suspended disbelief. In this study, we examined, empirically, the treatment and evidential impact of nonsignificant results. Our specific goals were twofold: to explore how psychologists interpret and communicate nonsignificant results and to assess how much these results constitute evidence in favor of the null hypothesis. First, we examined all nonsignificant findings mentioned in the abstracts of the 2015 volumes of Psychonomic Bulletin & Review, Journal of Experimental Psychology: General, and Psychological Science (N = 137). In 72% of these cases, nonsignificant results were misinterpreted, in that the authors inferred that the effect was absent. Second, a Bayes factor reanalysis revealed that fewer than 5% of the nonsignificant findings provided strong evidence (i.e., BF01 > 10) in favor of the null hypothesis over the alternative hypothesis. We recommend that researchers expand their statistical tool kit in order to correctly interpret nonsignificant results and to be able to evaluate the evidence for and against the null hypothesis.

Poster: Implementation and Evaluation of Cross Translation Unit Symbolic Execution for C Family Languages

Static analysis is a great approach to find bugs and code smells. Some of the errors span across multiple translation units. Unfortunately, it is challenging to achieve cross translation unit analysis for C family languages. In this short paper, we describe a model and an implementation for cross translation unit (CTU) symbolic execution for C. We were able to extend the scope of the analysis without modifying any of the existing checks. The analysis is implemented in the open source Clang compiler. We also measured the performance of the approach and the quality of the reports. The implementation is already accepted into mainline Clang.