Advancing best practices in data analysis with automatic and optimized output data analysis software
Current data analysis practice and statistics education are suboptimal in many senses, which contributes to the replication crisis. To address some of these issues, a new type of statistical and data analysis software solution is proposed here in which most of the analysis steps are compiled automatically based on the task and the measurement level of the variables and in which the result output is carefully optimized for informativeness and understandability. Automatic data analysis and optimized output can contribute to making data analysis more coherent across studies, remedying some aspects of the issues leading to the replication crisis, making analysis more efficient for users and helping to promote and teach better data analysis practices. Such a solution can be useful for researchers to conduct faster and more precise analyses, for students to see illustrations and demonstrations of data analysis solutions, and for methodologists to formulate straightforward analysis procedures that can promote more precise and more coherent data analysis practice in the literature. A possible implementation of such software, CogStat is presented here, in which additional design considerations make the results more understandable and more precise, the analyses more accessible, and the analysis more efficient.