Periodic Reporting for period 4 - Objectivity (Making Scientific Inferences More Objective)
Periodo di rendicontazione: 2019-05-01 al 2021-08-31
The project engages in improving the objectivity of scientific inferences. This is important because science undergoes a trust crisis at the moment. Replications of famous experiments yield much weaker effects than the original experiments. Questionable research practices in statistical inference are widespread in the scientific community, although they bias the results of research papers. Pressure to publish "significant" findings and the difficulties to publish inconclusive results lead to a biased research literature. In a time where powerful politicians publicly express their dismissal of science, it is more important than ever to strengthen the reliability of scientific research.
The project rethinks the notion of objectivity in science and uses its insights in order to develop more objective inference tools in three important domains of scientific reasoning: statistical hypothesis testing, inference about cause-effect relations, and inference to the best explanation of a phenomenon. It contributes to a more reliable, robust and objective science that can be trusted by policy-makers and the general public alike.
The project rethinks the notion of objectivity in science and uses its insights in order to develop more objective inference tools in three important domains of scientific reasoning: statistical hypothesis testing, inference about cause-effect relations, and inference to the best explanation of a phenomenon. It contributes to a more reliable, robust and objective science that can be trusted by policy-makers and the general public alike.
We argue for a new understanding of scientific objectivity, especially in the context of statistical inference. While previous research focused on objectivity as value freedom and correspondence to facts, we point out that important aspects of being objective involve questions such as transparency about assumptions and robustness to varying these assumptions. This perspective leads to a re-assessment of the objectivity of scientific inference procedures. For example, we conclude, seemingly paradoxically, that statistical inference based on subjective degrees of belief can be objective, too, and often it is even more objective than the standard method in the experimental sciences.
We then use our conceptual analysis of scientific objectivity to develop a new logic of hypothesis testing that balances philosophical considerations with practical requirements. Specifically, we show how our proposals for better statistical inference and a new credit reward system in science can help to overcome the replication crisis in science.
From a practical point of view, another important result is that we have given an operational meaning to objectivity in inference: that is, we have elaborated a checklist for the experimenter that he or she should follow when striving for objectivity in inference (e.g. in the design and analysis of experiments). Thus we have shown that quite abstract philosophical analysis is not only important for specialists, but it has a positive impact on very practical questions, too.
We then use our conceptual analysis of scientific objectivity to develop a new logic of hypothesis testing that balances philosophical considerations with practical requirements. Specifically, we show how our proposals for better statistical inference and a new credit reward system in science can help to overcome the replication crisis in science.
From a practical point of view, another important result is that we have given an operational meaning to objectivity in inference: that is, we have elaborated a checklist for the experimenter that he or she should follow when striving for objectivity in inference (e.g. in the design and analysis of experiments). Thus we have shown that quite abstract philosophical analysis is not only important for specialists, but it has a positive impact on very practical questions, too.
The contributions of the project tackle novel research questions in an unprecedented way, combining mathematical modeling, conceptual analysis, empirical data and computational techniques. Our research leads to a more nuanced assessment of scientific inference techniques, particularly in statistics, and ultimately to more reliable scientific inferences and conclusions. This has the potential to address the reproducibility crisis in science, to increase public trust in scientific inferences, and to integrate philosophical and scientific perspectives on scientific reasoning.