Reproducibility and Replicability in Science (2019) / Chapter Skim
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2 Scientific Methods and Knowledge
2 Scientific Methods and Knowledge
Pages 27-38
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From page 27... ...
We outline how scientists accu mulate scientific knowledge through discovery, confirmation, and correction and highlight the process of statistical inference, which has been a focus of recently publicized failures to confirm original results.
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With the work of such eminent figures as Copernicus, Kepler, Galileo, Newton, and Descartes, the scientific revolution in Europe in the 16th and 17th centuries intensified the growth in knowledge and understanding of the world and led to ever more effective methods for producing that very knowledge and understanding. Over the course of the scientific revolution, scientists demonstrated the value of systematic observation and experimentation, which was a major change from the Aristotelian emphasis on deductive reasoning from ostensibly known facts.
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From page 29... ...
Scientific progress is made when the drive to understand and control the world is guided by a set of core principles and scientific methods. While challenges to previous scientific results may force researchers to examine their own practices and methods, the core principles and assumptions underlying scientific inquiry remain unchanged.
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The core principles and assumptions of scientific inquiry embrace this tension, allowing science to progress while constantly testing, checking, and updating existing knowledge. In this section, we explore five core principles and assumptions underlying science: 1.
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In effect, all scientific disciplines seek to discover rules that are true beyond the specific context within which they are discovered. Knowledge Grows Through Exploration of the Limits of Existing Rules and Mutually Reinforcing Evidence Scientists seek to discover rules about relationships or phenomena that exist in nature, and ultimately they seek to describe, explain, and predict.
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The communal enterprise of science allows scientists to build on others' work, develop the necessary skills to conduct high quality studies, and check results and confirm, dispute, or refine them. Scientific results should be subject to checking by peers, and any scientist competent to perform such checking has the standing to do so.
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The advent of new scientific knowledge that displaces or reframes previous knowledge should not be interpreted as a weakness in science. Scientific knowledge is built on previous studies and tested theories, and the progression is often not linear.
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For example, the issue of drawing multiple statistical inferences from the same data is relevant for all hypothesis testing and in estimation. Studies involving hypothesis testing typically involve many factors that can introduce variation in the results.
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The probability that the null hypothesis is true, or that the alternative hypothesis is true, can be based on calculations informed in part by the observed results, but this is not the same as a p-value. In scientific research involving hypotheses about the effects of an intervention, researchers seek to avoid two types of error that can lead to non-replicability: 3 Text modified December 2019.
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From page 36... ...
In some cases, it may be useful to define separate interpretive zones, where p-values above one significance threshold are not deemed significant, p-values below a more stringent significance threshold are deemed significant, and p-values b etween the two thresholds are deemed inconclusive. Alternatively, one could simply accept the calculated p-value for what it is -- the probability of obtaining the observed result or one more extreme if the null hypothesis were true -- and refrain from further interpreting the results as "significant" or "not significant." The traditional reliance on a single threshold to deter mine significance can incentivize behaviors that work against scientific progress (see the Publication Bias section in Chapter 5)
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From page 37... ...
This approach starts with a priori (before data observation) assumptions, known as prior probabilities, and revises them on the basis of the observed data using Bayes' theorem, sometimes described as the Bayes formula.
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From page 38... ...
He designs an experiment to test the efficacy of a 1 percent solution that is then diluted 1 to 100, and then each subsequent dilution similarly diluted by 1 to 100 for a total of 1,000 dilutions. To avoid possible bias in the conduct of the experiment, the homeopathic practitioner enlists a researcher who, like the patients in the study, is unaware of whether any particular patient is receiving the dilution or pure distilled water (so called double-masked or double-blind study design)
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