Science is built on empirical evidence and testable explanations. It operates within a system designed to correct disproved ideas and continually build upon accurate, precise knowledge. Confidence in research results increases when those findings can be replicated by others using the same methods. Scientists are expected to make their data available for such replication.
Objectivity is the principle that scientific knowledge does not, or should not, be influenced by particular perspectives, value judgments, community bias, personal interests and the like. It is an ideal that underpins the legitimacy of science and its claim to authority in society. Many of the central debates in the philosophy of science have a connection with the idea of scientific objectivity. The logical rationale of this conception of objective science is that there are facts out there in the world, and it is the job of scientists to uncover them, analyze them, and systematize them. This concept is often criticized as a form of socially constructed science, but this does not imply that it is epistemically relativist. It can be challenging to communicate this concept of objective science. Scientists can erode their credibility by occasionally explaining how they have revised earlier research findings in the light of new evidence. This can encourage the false view that science is a linear progression toward incontrovertible truth.
In general, scientific knowledge enables us to make informed decisions. However, this knowledge must be relevant to have epistemic significance. This means that it must be useful for determining if something is true and justified (as opposed to untrue or even true but unjustified). Obtaining reliable scientific knowledge involves systematic observation, experimentation and comparison. Unlike other ways of knowing, science has strict rules about conducting research. These rules ensure that the resulting knowledge is reliable and trustworthy. In addition, they encourage scientists to honor their norms, which increases the public’s trust in them. This paper presents the foundational issues related to reliability as a new science. The key pillars of this science are described, and it can be considered a special case of risk and statistics sciences. In addition, links to other sciences are clarified. As this Venn diagram illustrates, much statistical knowledge can be meaningfully labeled as reliability science knowledge, but also much that cannot.
Reliability is a measure of how consistently an outcome can be replicated. It is different from validity, which measures the accuracy of a result. For example, it is reliable if you count the same thing three times and get the same effects each time. Reliable data can save organizations valuable time and money by reducing the need for costly double-checks and corrections. It also helps ensure that decision-makers can make informed decisions using accurate information. However, some tensions remain between scientists, Indigenous and local knowledge holders, and the use of scientific information and knowledge in decision-making. These tensions can be related to how research questions are framed, how science is performed, and how policymakers and other stakeholders use it. This includes how scientists communicate their work with Indigenous and community audiences and incorporate Indigenous knowledge into their science.
Reproducibility is an important part of any scientific endeavor. It requires that scientists make all their data and code available to others. This allows other researchers to verify the results and improve upon them. This process also helps identify mistakes that may have been made during research.
Any field or science needs to question and scrutinize its core knowledge continuously. This core knowledge comprises concepts, theories, principles, approaches, methods and models. Moreover, these ideas must be validated through continuous interaction and communication among scientists. This process is called critique, an essential part of any scientific system.
Reproducible research is the best way to build confidence in scientific findings. The public will likely trust a study more if it is transparent about its data and methods. According to a 2019 survey, the public recognizes several key signals indicating whether a scientific study is trustworthy. These include whether or not an investigation is reproducible if it demonstrates that it can be reproduced, and if the authors are clear about their results.