Is there any Bias in Science?
It is essential to recognize there are two distinct types of scientific research. They can be called: Foundational Science and Complex Science. The theories of Foundational Science are robust, simple, can account for apparent anomalies, and don't include ad hoc explanations. The theories of Complex Science are less robust, often complicated, routinely fail to account for anomalies, and include conspicuous ad hoc explanations
The role of science is to establish what is measurable, achieve accurate and precise measurements, and to ensure every effort is made to falsify its theories.
Bias in science is revealed by the extent scientists fall short in their logic, fail to address contradictions, or opine about matters that have neither been measured nor proven
The general understanding within academia is that any biases that may occasionally intrude themselves into scientific knowledge will always be weeded out. The scientific community also stresses that the checks and balances in the Scientific Method successfully resolves and defuses any biases they may have; and that this immunity is thought to be unavailable to those outside research or university circles.
Our work shows that these statements are appropriate for Foundational Science (which includes classical physics and chemistry) but neither are foolproof for Complex Science (which includes most specialties that cannot be verified in a controlled laboratory setting) or theoretical science (including theoretical physics). The distinctions between the two types are shown in the graphics (Methods A and B) below.
The method to spot biases in complex science uses a modified version of the general description*, namely: bias in complex science can be detected by:
An examination of the logic, data, anomalies, and assumptions used to support a particular scientific theory; as compared to the logic, data, anomalies, and assumptions used to reject any competing theory
Science, by definition, must be unbiased. So a careful use of the above bias description is a highly useful "flag" when scientific findings are interpreted for ideological ends.
* The general description of bias: An over-emphasis or focus on the negative (positive) aspects of an object, and an under-statement or disregard of the positive (negative) aspects of the object, as compared to any competing object.
Nature is our kindest friend and best critic in experimental science if we only allow her intimations to fall unbiased on our minds
- Michael Faraday - Discoverer of scientific laws of electromagnetism
I have approximate answers and possible beliefs in different degrees of certainty about different things, but I’m not absolutely sure of anything.
Richard Feynman - Physicist
We should ... abandon the distinction between scientific and non-scientific thought. The proper distinction is between logical and non-logical thought
C. S. Lewis - Author
Our research shows the well established "scientific method", as usually defined, is inaccurate. This is because there is not a single scientific method but two very distinct methods - A and B - shown in the graphics below.
Foundational Science (A) relies only upon the idea the universe is real and behaves in a consistent manner which can be understood with logic and applied mathematics. Called foundational assumptions they are accepted as the minimum starting point for scientific work. The central feature of Foundational Science is that decisive tests to refute any idea are possible to carry out.
Complex Science (B), on the other hand, involves further uncertainties that need to be assessed for the research work to progress. Decisions on the best way to proceed means that the research plan necessarily involves some human judgement. Any test will no longer provide a definitive answer but simply contribute data that is either plausible, or not, depending upon the soundness of the original assumptions. The research work therefore contains a degree of circularity - meaning that the assumptions, to some degree, become the conclusions.
Formal Logic, as a means to demonstrate the certainty of a complex scientific theory (e.g.: those involved in astrophysics and emergence), tends to be inconclusive. As a result scientists make inferences with varying standards of proof for different explanations. Recognizing those differences provides a Bias Flag
It is not a censure of complex science to say decisive tests to refute a multi-faceted question are not feasible. A quick example will help clarify why assumptions are necessary in Complex Science. Imagine you are a researcher and wish to find out whether a particular medication can cause negative side-effects. Being resourceful you test it three different ways: 1] using animal test analogues; 2] by use of a large medical data-base on health and life-styles; and 3] by your own correlation study developed with the cooperation of patients at the university hospital. Each approach raises questions and demands assumptions: what dosage should be used, which type of animal should be tested, is the database applicable and reliable, how should available money be used - an increased sample size or higher quality data from each subject - and does correlation demonstrate causation? The final issue: you realize that a finding which shows there are no negative side-affects could be perceived as not being rigorous or helpful - assuming, of course, you are not funded by the medications' manufacturer.
It is clear Complex Science allows bias to get its foot in the door as the assumptions selected will accordingly affect the findings of the study.
The goal of Complex Science is to progress by Method B until all assumptions are removed and the science can be classified as "Foundational". However, this can be difficult when there are many items that need to be controlled (as in the above example).
Method B has a uneven record of success. Some complex questions do get definitively resolved; but after many years of investigation far too many "scientific findings" are supported by historical assumptions. Thomas Kuhn's fifty-year-old paradigm shift idea can now be extended to say research would be more efficient if the biases involved were better understood and suitably managed.
The lack of focus in addressing significant inconsistencies in various scientific theories provides a significant Bias Flag
For many people biases about science flourish not so much from what they don't know, but from learning so much that isn't scientifically correct
Many people learn about science from the opinions of the biased
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