Logic and Critical Thinking in the Biomedical Sciences: Volume I: Deductions Based Upon Simple Observations

By Jules J. Berman

Science is not a collection of facts. Science is the process by which we draw inferences from facts. Volume I of Logic and Critical Thinking in the Biomedical Sciences invites readers to linger over a collection of common observations to see what inferences can be drawn, when one applies a bit of deductive logic. If we just think about what we observe, it is often possible to discover profound biomedical insights.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences.

• Provides a strong introduction to deductive methods that can be directly applied to the biomedical sciences
• Using hundreds of examples, shows how creative scientists draw important inferences from observations that are often ignored by their peers
• Discusses complex biological and medical concepts in a relaxed manner, intended to focus the reader’s attention on the deductive process, without dwelling excessively on details

• Language: English
• Publisher: Academic Press
• Release date: Jul 3, 2020
• ISBN: 9780128213711

Jules J. Berman

Jules Berman holds two Bachelor of Science degrees from MIT (in Mathematics and in Earth and Planetary Sciences), a PhD from Temple University, and an MD from the University of Miami. He was a graduate researcher at the Fels Cancer Research Institute (Temple University) and at the American Health Foundation in Valhalla, New York. He completed his postdoctoral studies at the US National Institutes of Health, and his residency at the George Washington University Medical Center in Washington, DC. Dr. Berman served as Chief of anatomic pathology, surgical pathology, and cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred to the US National Institutes of Health as a Medical Officer and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past President of the Association for Pathology Informatics and is the 2011 recipient of the Association’s Lifetime Achievement Award. He is a listed author of more than 200 scientific publications and has written more than a dozen books in his three areas of expertise: informatics, computer programming, and pathology. Dr. Berman is currently a freelance writer.

Book preview

Logic and Critical Thinking in the Biomedical Sciences

Volume I: Deductions based upon simple observations

First Edition

Jules J. Berman

Table of Contents

Cover image

Title page

Copyright

Other books by Jules J. Berman

Dedication

About the author

Preface

Abstract

1: Introduction to biomedical logic

Abstract

Section 1.1 What is reasoning?

Section 1.2 What is proof?

Section 1.3 Fallacies

Section 1.4 Can several weak arguments substitute for one strong argument?

Glossary

2: Drawing inferences from photographs

Abstract

Section 2.1 Thinking about what we see

Section 2.2 Looking at nuclei

Section 2.3 Deducing that the chloroplasts in plant cells are captured cyanobacteria

Section 2.4 Phylogenetic advances manifest themselves in the embryo

Section 2.5 All eutherian cell types are equivalent among classes of species

Section 2.6 Length of gestation in eutherian animals

Section 2.7 Inferring that there are many more than 200 cell types

Section 2.8 What we learn from looking at red blood cells

Section 2.9 What we learn from inclusion bodies

Glossary

3: Inferences drawn from organismal development

Abstract

Section 3.1 Desmosomes, the essence of animals

Section 3.2 Ear ossicles missing from reptiles

Section 3.3 The wisdom of teeth

Section 3.4 The development of bones

Section 3.5 Unnecessary cerebellum

Section 3.6 Mammalian hair and the origin of basal cell carcinoma

Section 3.7 Inferences drawn from the shape of snake heads

Section 3.8 Depth of penetration of ultraviolet light

Section 3.9 Evolution is often a one-way road

Glossary

4: Inferences drawn from temporal sequences

Abstract

Section 4.1 Paradoxes of creation

Section 4.2 A logical approach to bootstrapping paradoxes

Section 4.3 Inferences drawn from the sequence of clinical events preceding death

Section 4.4 When was aging invented?

Section 4.5 When does aging begin?

Section 4.6 Old age does not cause cancer

Section 4.7 What can we infer when we know the time at which a mutation occurred?

Glossary

5: Finding relationships among biological entities

Abstract

Section 5.1 Defining relationships and similarities

Section 5.2 Ancestral genes

Section 5.3 The significance of gene sequence conservation

Section 5.4 Unexpected gene conservation

Section 5.5 Relationships between human diseases and orthodiseases

Section 5.6 Inferring the relationships between genetic diseases and their phenocopies

Section 5.7 The logic of treating disease pathways, not disease genes

Glossary

6: Drawing inferences from classifications and ontologies

Abstract

Section 6.1 What is a classification?

Section 6.2 Ontologies

Section 6.3 Some paradoxes of classifications

Section 6.4 The classification of living organisms and the meaning of species

Section 6.5 Speciation is the primary driver of evolution

Section 6.6 Classifications allow us to discover class-specific treatments of diseases

Glossary

7: Biomedical advances achieved by reducing class noise

Abstract

Section 7.1 Significance of class noise

Section 7.2 Why fungi are definitely not plants?

Section 7.3 Naegleria fowleri is not an amoeba

Section 7.4 Clinical trials for staged cancers

Section 7.5 Psychiatric illnesses

Section 7.6 Cures for the most common and chronic diseases

Section 7.7 The mistake of overclassifying in an effort to avoid class blending

Glossary

8: How a little logic could have corrected long-held misbeliefs

Abstract

Section 8.1 We should have known better

Section 8.2 An embryo is not a miniature baby

Section 8.3 The genome is a recipe book, not a blueprint

Section 8.4 Why our genome is full of junk

Section 8.5 Bacteria live in our stomachs

Section 8.6. We can abandon Koch’s postulates

Section 8.7 Diseases arise through a sequence of events that occur over time

Section 8.8 The egalitarian nature of logical analysis

Glossary

Index

Copyright

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Dedication

For Everett

About the author

Jules J. Berman received two baccalaureate degrees from MIT, in Mathematics and in Earth and Planetary Sciences. He holds a PhD from Temple University and an MD from the University of Miami. He was a graduate student researcher in the Fels Cancer Research Institute, at Temple University and at the American Health Foundation in Valhalla, New York. His postdoctoral studies were completed at the US National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, DC. Dr. Berman served as chief of anatomic pathology, surgical pathology, and cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred to the US National Institutes of Health as a medical officer and as the program director for pathology informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past president of the Association for Pathology Informatics and the 2011 recipient of the Association's Lifetime Achievement Award. He has first authored more than 100 journal articles and has written 20 science books. His recent titles, published by Elsevier, include the following:

Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 1st edition (2012)

Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information (2013)

Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases (2014)

Repurposing Legacy Data: Innovative Case Studies (2015)

Data Simplification: Taming Information with Open Source Tools (2016)

Precision Medicine and the Reinvention of Human Disease (2018)

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition (2018)

Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 2nd edition (2019)

Evolution's Clinical Guidebook: Translating Ancient Genes into Precision Medicine (2019)

Preface

Abstract

Science is not a collection of facts. Science is the process by which we draw inferences from facts. Asking students to absorb a large body of facts is a poor substitute for teaching science. Volume I of "Logic and Critical Thinking in the Biomedical Sciences" asks readers to temporarily stop memorizing facts and to begin thinking about observational information. As we linger over a collection of common observations and apply a bit of deductive reasoning, we can begin to draw inferences. By doing so, we can often discover profound biomedical insights. In many cases, we can use a logical analysis to correct old misconceptions that were drawn hastily or without sufficient introspection.

Keywords

Deduction, Reasoning, Inferencing, Experimental evidence, Scientific method

It’s all about you, using your own mind, without any method or schema, to restore order from chaos.

Marcel Danesi, author of The Puzzle Instinct: The Meaning of Puzzles in Human Life

In the early years of my training as a medical scientist, the leader of my research group was fond of repeating the following motto: Pathology is an experimental science! By this, he meant to say that pathology had moved beyond the point where pathologists built elaborate theories of disease based on obscure morphologic findings in tissues. He did not want me, or any of the other students under his supervision, to waste time building theories. He wanted us to perform carefully controlled experiments designed to answer questions related to the group’s focus.

The experimental method had proven remarkably successful in advancing medical science in the preceding decades. Before the 1970s experimental scientists had resolved the structure of DNA and had broken the genetic code. Experimentalists had provided us with dozens of drugs effective against tuberculosis and other bacterial infections and vaccines that prevented infections by polio and other serious diseases of childhood. Researchers had developed new types of imaging modalities that were making diagnosis easier and more accurate. We had entered a golden age of medical advancement, due almost entirely to scientists who believed in experimentation, not sophistry. Experimentation was exalted to such a high level that it had become synonymous with the scientific method, as though any other approach to knowledge discovery was unscientific.

While all this experiment-driven progress was going on, a large number of classically trained pathologists, who were taught to study the morphology of diseased tissues but who lacked training in the newest laboratory techniques, were feeling neglected. A rift was forming between the anatomic pathologists who performed clinical services (e.g., autopsies and tissue examinations) and the pathologists who performed experiments in laboratories. The anatomic pathologists enjoyed building logical arguments in support of their own interpretations of various observations made upon diseased tissues. Using the power of observation, without relying on experimental validation, they published manuscripts that could only be understood by their fellow anatomic pathologists. Likewise the experimental pathologists, working in nonhuman model systems, published manuscripts that were often dismissed as irrelevant to the human condition by clinicians.

Last but not the least, the NIH (National Institutes of Health) and other governmental agencies had become flush with money to support hospital-based research. The experimentalists were in a position to benefit directly from a seemingly inexhaustible supply of research funds. The nonexperimental pathologists, who fancied themselves the deep thinkers of the medical world, could not compete with the experimentalists. Grant applicants were judged primarily on the basis of the experimental approach. There was no money to support the deep thinkers and medical philosophers.

Knowing, as we did, that the emphasis of all medical funding would be determined by the quality of experiments, it is no wonder that laboratory technique was emphasized, while the fundamentals of biological reasoning were largely ignored. Consequently, logic is not taught at the graduate level in any of the biomedical sciences. It was my experience that students who expressed much interest in theorizing were strongly discouraged from doing so. After one of my scientific presentations, in which I may have drawn a few too many logical inferences from my meager experimental data, I was stopped midlecture by one of the professors who said, You missed your calling. You should have become a Talmudic scholar. This remark was not intended as a compliment. The professor was implying that I was not cut out for the experimental life of a modern scientist. It seems that I was best suited to arguing over obscure passages in ancient texts with like-minded unemployed scientists, with time on their hands.

Nearly a half century has passed since the start of my career in medicine. During this time the world has seen marvelous advancements in medicine. Nearly all of these advances have been based on experimental studies, not on logical deductions drawn from observations of the natural world. Why then have I written a two-volume opus devoted to Logic and Critical Thinking in the Biomedical Sciences?

Although society has greatly benefited from experimental studies, several recent developments in the culture and the processes of modern biomedical research have pressed the need for training in logical reasoning.

1.Logic protects us from nonsensical conclusions drawn from unintuitive analytic methods

As our experimental data grow in size and complexity, we have come to rely on so-called deep analytics—collections of powerful computational algorithms that are based largely on correlative comparisons or on feedback processes that automatically assign weights to variables. Relationships among input variables emerge without the benefit of a chain of reasoning developed from proven premises. Try as we might, we can seldom logically explain the conclusions drawn from such methods. Scientists have grown complacent. On occasion, the results of deep analytics hold true for a narrow set of circumstances, but when instances occur when the predicted outcomes do not apply, we have no way of determining what went wrong. Isn’t it about time that we begin to scrutinize complex data with the same rules of logic that apply to everything else in our universe? One of the purposes of Logic and Critical Thinking in the Biomedical Sciences is to show us how we can look at our own data and draw inferences that deep analytic algorithms would never discover. Moreover the logical analysis of data can often serve as a check on computer-generated conclusions that are biased, misleading, absurd, or catastrophic.

2.Logic skills are required to conceptualize, design, and interpret experiments

It would be comforting to assume that, in the present day, science has advanced to the point that experiments are reliable and that we can assume that a modern experiment, conducted in a prestigious academic center and subject to a rigorous peer review process, can be accepted as settled truth. Unfortunately, this is not the case. Many of the recent medical advances published in biomedical journals have been shown to be wrong or unrepeatable or even fraudulent.¹–²² Today the experimental conclusions published in scientific journals must be considered tentative, until such time as the results can be repeated. In the case of analytic studies performed on private or public data sets, published conclusions must be validated following rigorous reanalysis by other data scientists.²³

In the past two decades, we have learned that animal genomes and biological systems in general operate under layers of complexity that we had not previously imagined.²⁴,²⁵ As our biomedical data becomes larger and more complex, we find it increasingly difficult to fathom what any of our data truly signifies. Now, more than in previous decades, we need scientists who are well versed in the logical design of experiments and in the proper methods of collecting, annotating, and tracking large quantities of data that accrues over time.²⁶

As it stands, nearly all the data currently produced by biomedical laboratory scientists and collected from clinical encounters are unusable. We cannot make sense of it because it isn’t organized in a way that supports meaningful analysis.²⁷ In this book, we will learn how to avoid some of the common mistakes in the design, execution, and analysis of large experiments and clinical trials. We shall also see how we might distinguish valuable data from uninformative garbage.

3.Some of our greatest scientists are terrible at laboratory benchwork

Finally, it is important to accept that some of our greatest scientists were never suited for laboratory work. For example, Wolfgang Pauli (1900–58) was one of the most highly respected theoretical physicists of his time, but he was a notoriously bad experimentalist. His mere presence in a laboratory was enough to cause experiments to fail. The experimental physicist, Otto Stern, famously barred Pauli from entering his workplace. The two colleagues communicated by shouting to one another, through a closed doorway. For hundreds of years, virtually all biologists participated in laboratory and field work as mandatory components of their academic training. This custom made sense when we lived in a world with relatively little available data. Today, scientists have access to more data than they can fully analyze. There is no necessity for every biomedical scientist to generate more data. We are seeing a shift away from the scientist working alone in his/her laboratory, as we consolidate large permanent informational databases built from measurements gathered from multiple sources. These data sets include troves of clinical information collected from thousands or even millions of individual patients. Geneticists, bioinformaticians, statisticians, and data analysts trained in a wide range of medical disciplines have access to communal data resources and are drawing their own inferences from such data. The NIH is returning to the preexperimental roots of science and is awarding grants to scientists whose chief goal is to utilize existing data records. At last, those of us with a philosophical bent can contribute just by thinking about what our collected data are trying to tell us. This book will restore a measure of self-respect to all of the great biomedical theorists who are repelled by modern research laboratories.

Science is not a collection of isolated facts.

If you pick up any undergraduate science text, you’ll find the pages filled with facts and figures. We were all forced to commit fragments of knowledge to memory and to regurgitate it all on our final exams. It is truly amazing that any college graduate pursues a career in the sciences. Most scientists come to realize that science is not about facts; science is about the general relationships that we can deduce from facts. In Logic and Critical Thinking in the Biomedical Sciences, we stress what we can deduce from common observations. In many cases, we’ll take trivial observations that seem to have no connection to anything, and we’ll solve profound mysteries of medicine and biology, by means of simple deduction. Students and professionals who feel overwhelmed by the sheer number of facts that they are expected to master might find some solace in this book.

In summary the goal of Logic and Critical Thinking in the Biomedical Sciences is to provide readers with a logical approach to analyzing information in the field of medicine and its related biological disciplines. To do so, we will be reviewing hundreds of examples wherein faulty reasoning has led to incorrect conclusions that have resulted in errors in diagnosis or treatment and have generally impeded the advancement of medical science. We’ll see that the application of logic might have saved us a great deal of time, expense, and energy. Readers of this book will profit by avoiding the mistakes of their predecessors.

How to read this book

Each chapter comes with its own reference section and its own glossary. Rather than filling the corpus of text with descriptions and definitions, I packed the glossaries with terminology and explanations of specialized techniques. On occasion, I included snippets of source code written in Python, Perl, or Ruby, just in case any of the readers wanted to write their own software programs to assist in the analysis of data. Many of the glossary items allow me to expand on topics that are a bit too digressive for inclusion within the text. Other glossary items are highly relevant to the text, but are not strictly necessary for the reader’s comprehension of the narrative. The chapter glossaries can be read as stand-alone documents.

The book contains many different logical inferences, way too many for any reader to remember. I thought it might be useful to provide a trick whereby readers can collect and peruse all the inferences, without the explanatory text. For lack of a better idea, every inference is consistently preceded with the pompous conjunctive: Hence (e.g., I think. Hence, I am.). You won’t find a single therefore in this book, aside from what appears in this sentence, and in quoted passages. By means of an incommodious reliance on the word hence, readers of the e-version of this book can locate every henced inference, via the search box. Hence, readers can inspect every conclusion herein for purposes of amusement, erudition, or criticism.

In preparing to write Logic and Critical Thinking in the Biomedical Sciences, I reviewed a number of classical texts, dating back to the early 20th century.²⁸–³⁰ These excellent texts are a bit too stodgy and dry for modern sensibilities. To mitigate the pain of reading any book that purports to teach readers how to think, I’ve adapted a first person storytelling style. Consequently the volumes do not read like a typical textbook on logic or data interpretation. Issue of style notwithstanding my hope is that this book will serve as a valuable introduction to the logic of data analysis, suitable for students and professionals in any of the biomedical sciences.

References

[1] Anon. Unreliable research: trouble at the lab. The Economist. 2013 October 19.

[2] Kolata G. Cancer fight: unclear tests for new drug. The New York Times. 2010 April 19.

[3] Ioannidis J.P. Why most published research findings are false. PLoS Med. 2005;2:e124.

[4] Baker M. Reproducibility crisis: blame it on the antibodies. Nature. 2015;521:274–276.

[5] Naik G. Scientists' elusive goal: reproducing study results. Wall Street J. 2011 December 2.

[6] Innovation or Stagnation. Challenge and opportunity on the critical path to new medical products. U.S. Department of Health and Human Services, Food and Drug Administration; 2004.

[7] Hurley D. Why are so few blockbuster drugs invented today?. The New York Times. 2014 November 13.

[8] Angell M. The truth about the drug companies. In: The New York Review of Books. . 2004;Vol. 51 July 15.

[9] Crossing the Quality Chasm. In: Quality of Health Care in America Committee, eds. A new health system for the 21st century. Washington, DC: Institute of Medicine; 2001.

[10] Wurtman R.J., Bettiker R.L. The slowing of treatment discovery, 1965-1995. Nat Med. 1996;2:5–6.

[11] Ioannidis J.P. Microarrays and molecular research: noise discovery?. Lancet. 2005;365:454–455.

[12] Weigelt B., Reis-Filho J.S. Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry. Breast Cancer Res. 2010;12:S5.

[13] Personalised medicines: hopes and realities. London: The Royal Society; 2005. Available from: https://royalsociety.org/~/media/Royal_Society_Content/policy/publications/2005/9631.pdf.

[14] Vlasic B. Toyota's slow awakening to a deadly problem. The New York Times. 2010 February 1.

[15] Sanghavi P., Jena A.B., Newhouse J.P., Zaslavsky A.M. Outcomes after out-of-hospital cardiac arrest treated by basic vs advanced life support. JAMA Intern Med. 2015;175:196–204.

[16] Lanier J. The complexity ceiling. In: Brockman J., ed. The next fifty years: sscience in the first half of the twenty-first century. New York: Vintage; 2002:216–229.

[17] Ecker J.R., Bickmore W.A., Barroso I., Pritchard J.K., Gilad Y., Segal E. Genomics: ENCODE explained. Nature. 2012;489:52–55.

[18] Rosen J.M., Jordan C.T. The increasing complexity of the cancer stem cell paradigm. Science. 2009;324:1670–1673.

[19] Labos C. It ain't necessarily so: why much of the medical literature is wrong. Medscape News Perspectives. 2014 September 09.

[20] Gilbert E., Strohminger N. We found only one-third of published psychology research is reliable – now what?. The Conversation August. 2015. ;27:. Available at: http://theconversation.com/we-found-only-one-third-of-published-psychology-research-is-reliable-now-what-46596.

[21] Prasad V., Vandross A., Toomey C., Cheung M., Rho J., Quinn S., et al. A decade of reversal: an analysis of 146 contradicted medical practices. Mayo Clin Proc. 2013;88:790–798.

[22] Ferguson C., Marcus A., Oransky I. The peer review scam. Nature. 2014;515:480–482.

[23] Berman J.J. Repurposing legacy data: innovative case studies. Waltham, MA: Morgan Kaufmann; 2015.

[24] Madar S., Goldstein I., Rotter V. Did experimental biology die? Lessons from 30 years of p53 research. Cancer Res. 2009;69:6378–6380.

[25] Lazebnik Y. Can a biologist fixa radio? Or, what I learned while studying apoptosis. Cancer Cell. 2002;2:179–182.

[26] Berman J.J. Principles of big data: preparing, sharing, and analyzing complex information. Waltham, MA: Morgan Kaufmann; 2013.

[27] Berman J.J. Data simplification: taming information with open source tools. Waltham, MA: Morgan Kaufmann; 2016.

[28] Mill J.S. A system of logic, ratiocinative and inductive, being a connected view of the principles of evidence, and the methods of scientific investigation. Eighth edition Franklin Square, New York: Harper and Brothers; 1882.

[29] Atkinson W.W. The art of logical thinking; or, the laws of reasoning. Chicago, IL: The Progress Company; 1909.

[30] Read C. Logic: deductive and inductive. 4th edition Marshall, Hamilton, Kent and Company, London: Simpkin; 1914.

1

Introduction to biomedical logic

Abstract

Reasoning involves using the relationships among assertions to draw a true inference. The basic unit of reasoning is the syllogism, which takes the form: assuming assertion A and B are true, then we can conclude the assertion C is true. A logical argument involves chaining syllogisms to draw a new inference. Because syllogisms can be examined easily, they serve as an excellent model for uncovering faulty reasoning, and most books on logic contain dozens of examples of fallacious inferences drawn from deceptively constructed syllogisms. In this chapter, we will discuss common fallacies selected from the field of biomedicine. We will also discuss the concept of proof, as it is known to biologists, and how it differs from proof, as it is known to mathematicians. We will see that biologists can seldom prove anything, with certainty, but lacking such proof does not prohibit us from establishing physical laws and generalizations based on the relationships among observable biological processes.

Keywords

Syllogism; Induction; Deduction; Fallacious reasoning; Weight of evidence; Tentative conclusions

Chapter outline

Section 1.1. What is reasoning?

Section 1.2. What is proof?

Section 1.3. Fallacies

Section 1.4. Can several weak arguments substitute for one strong argument?

Glossary

References

Couldn't Prove Had to Promise.

Title of book of poems by Wyatt Prunty

Section 1.1 What is reasoning?

It is difficult to cover so basic a concept as reasoning without stepping on somebody's toes. Mathematicians, experimentalists, engineers, and cognitive scientists might all have their own ways of thinking about thinking. Nonetheless, we must begin somewhere, and I personally prefer the approach of William Walker Atkinson, author of The Art of Logical Thinking (1909) who wrote, "A thought is a mental product which embraces the relation of two or more ideas.¹" Reasoning would be the act of using thoughts to develop new ideas or knowledge.

Reasoning is synonymous with inferencing and is often divided into two types: inductive and deductive reasoning. In deductive reasoning, we use our knowledge of some general truth to learn something particular. In inductive thinking, we use our knowledge of particulars to make some general truth. In practice a line of reasoning may involve several or many steps, using both inductive reasoning and deductive reasoning in the process. It should not be surprising that the word deduction, in contemporary parlance, is used to describe almost any type of reasoning.

The basic form in which reasoning is expressed is the syllogism. A syllogism, in logic, is analogous to the haiku, in poetry. Each must follow a very specific form, and each is intended to create a beautiful and simple statement embracing a truth. In the case of a syllogism, there must be three parts: two premises followed by a conclusion:

A (Premise) B (Premise)

hence, C (Conclusion)

For example:

Man is mortal Socrates is a man hence, Socrates is mortal

We could have switched the order of the two premises:

Socrates is a man Man is mortal hence, Socrates is mortal

Note that, in this following case, we cannot switch a conclusion for a premise:

Socrates is mortal Socrates is a man hence, Man is mortal (correct, but based on a false inference)

What is it that we are really doing when we indulge in syllogistic arguments? For the most part, we are drawing upon what we know about the relationships of things, and this often means we are using some system of classification that establishes what we can say about individual things or classes of things. Our faith in the syllogism is based almost entirely on our faith in the way we classify objects.

For example:

All animals have a head A horse is an animal hence, A horse has a head

All animals have a head is a statement of a fundamental property that applies to every member of Class Animal, within the classification of living organisms. In the field of biology, many of the most trusted syllogisms come from our use of and dependence on, the classification of living things and the various physical and chemical processes that characterize species. Through the use of classifications, we use the relationships among classes of organisms to discover new biological principles.

As it happens, the syllogism above is false, insofar as there are many types of animals that do not have an anatomically recognizable head, for example, sponges. Class Craniata is defined as the class of animals having a cranium of sorts and having a cranium is all it takes to have a head. Hence a syllogism that yields a true conclusion would be: [Glossary Craniata]

All animals of Class Craniata have a head A horse is an animal of Class Craniata hence, A horse has a head

As we use and test our classifications, we learn more and more about the contained classes and the relationships of one class to another. The science of classification, known variously as taxonomy or as systematics, and its logical applications to the fields of biology and medicine, will be discussed in detail in Volume I, Chapter 6, Drawing inferences from classifications and ontologies. [Glossary Systematics, Taxa, Taxon, Taxonomic order]

Before leaving the topic of syllogisms, it is important to note that even the most inspired syllogism has negligible value as a stand-alone concept. When we reason, we are actually tying together many different syllogisms to build an argument. An outstanding, but somewhat disturbing, example of an argument based upon syllogisms is the famous proof, proffered by Bertrand Russell and Alfred North Whitehead that 1 + 1 = 2. The proof occupies the first several hundred pages of the Principia Mathematica.² The story is told that his all-out effort to prove that 1 + 1 = 2 exhausted Bertrand Russell to the point that he was never again fit for the task of producing an oeuvre equal to his Principia. Nonetheless, tiny syllogisms are the basic units of logic. Scientists untrained in logic may be unaware of the importance of syllogisms, but without them we could never prove anything, and we would never be quite certain that 1 + 1 = 2 (Fig. 1.1).

Fig. 1.1 The last few lines of a long proposition, extending over several hundred pages, proving that 1 + 1 = 2. Source: Principia Mathematica by Alfred North Whitehead and Bertrand Russell, 1910 (2).

Section 1.2 What is proof?

Mathematicians are the only humans who can honestly prove anything. My mathematically endowed friends are fond of insisting that even God must obey the conclusions drawn from a mathematical proof. For those of us who labor as biologists or medical scientists, our concept of proof is a cheap facsimile of the real deal. We have our highly suggestive observations that seem to justify one hypothesis over another, and we have our unbroken biological laws upon which we depend. Yet, when it comes to proof, we always come up short. Fundamentally, every biological assertion rests upon observations that we can never fully understand. The statement that viruses are lifeless fragments of nucleic acid wrapped in a protein envelope cannot be stated with any certainty because we do not understand the origin of viruses; hence, we do not know whether ancestral viruses were living organisms. Furthermore, we do not understand all of the functions performed by viruses, or the purposes of every function that we observe. We do not, in fact, know the full variety of viral species, and whether some of those species may qualify as living organisms. We do not understand the ultimate destiny of viruses, or whether these seemingly inanimate organisms may be evolving toward a form of existence that lies outside our comprehension. More profoundly, our ignorance of viruses is matched by our ignorance of just about every seen and unseen member of the natural world.

Basically, we can never be certain of our conclusions, when we know so little about our world. Everything we do and say is tentative and cannot be absolutely proven. Nonetheless, we persevere by insisting upon a set of criteria that provide us with a high degree of confidence in our conclusions. Here are the various requirements of a credible biological theory:

1.The theory must be built logically.

Simply because we cannot offer proof in the manner practiced by mathematicians, we are not excused from behaving logically. In the world of natural sciences, we use syllogisms to build our conclusions. The premises upon which the syllogisms are built are based on observations, and the observations must be validated to the extent that any assertions are validated. Furthermore, all of the inferences drawn from such syllogisms must conform to the basic rules of logic.

2.The theory must adhere to all the known facts.

We cannot pick and choose the observations that ought to fit the theory. If there are any factual exceptions to the rule, then the rule is no longer valid. This generally means that scientists have an obligation to compare their findings with those of others working in their field. If the observations and conclusions of one scientist are contrary to those of others, then the scientists who proposes a theory need to reconcile any disparate observations as best as possible. This practice often leads to constructive modifications of the original theory.

It is worth noting here that scientists are seldom trained to deal constructively with disputed observations. Years ago, I attended a seminar presented by a respected scientist who had formed a set of conclusions based upon a set of carefully measured experimental observations. I was aware of another scientific group that had approached the same problem with their own experiments, only to produce a different set of data that resulted in an opposite conclusion. After her presentation, there was an open question period, and I asked, as nicely as I could, how she reconciled her group's findings with those of the other laboratories. The researcher turned red with anger, clearly thinking that I was questioning her personal integrity and the integrity of her associates. She told the audience that her role as a scientist involved producing accurate data, leading to an objective conclusion. She did not feel responsible for the output of other laboratories, and she did not feel the need to defend her conclusions against those of other laboratories. Of course, she refused to answer my question.

A pity. In retrospect, I have come to understand that we live in a world of apparent contradictions and that there is much to be learned when we try to account for different observations. Two laboratories might conduct an experiment under different conditions, controlling a different set of variables or measuring different outcomes. The methods of data analysis of one laboratory may be different from the methods used by another lab. In many cases, apparent contradictions are actually complementary findings that could lead to a more robust or more generalizable conclusion, if properly analyzed.

Bertrand Russell maintained that, The most savage controversies are those about matters as to which there is no good evidence either way. I would agree. When irreconcilable differences arise in science, it is always best to reexamine the data, to see if there is a fresh approach that will make the controversy go away. A fine example wherein a fresh examination of data led to a major breakthrough in science came from the wave versus particle theories of light. Both theories had their champions, and the controversy loomed large until it was shown, to everyone's satisfaction, that light has the dual properties of wave and particle. Likewise, the supporters of the viral theory of carcinogenesis and of the mutational theory of carcinogenesis fought among themselves for years until the oncogene theory emerged, with elements preserved from both camps.

3.The theory is testable, the tests must be repeatable, and none of the tests must disprove the theory.

Unless there is a way to test the theory, by experiment or by observation, then the theory cannot be proven. Untestable assertions cannot be accepted as viable scientific theories and have been traditionally denigrated as pseudoscience.

4.Alternate explanations of results must be considered.

A proof implies that the conclusion is true and that contradictory conclusions are false. If there are valid alternate theories, then we really cannot say that a proof has been attained, even if the logic of our proof seems to be correct. In such a case, our theory would be consigned as one among several alternate explanations.

When we speak of alternate theories, we are restricting ourselves to alternate theories built from logical reasoning whose premises can be examined and whose final conclusions can be tested. Scientists should not be dismissive of the personal belief systems of their detractors, but scientists need not attempt to debate assertions that have no scientific basis and cannot be tested.

5.All future observations are consistent with the theory.

The theory must be true forever. Of course, this is a tall order, and we cannot expect scientists to spend eternity waiting for vindication. Nonetheless, every scientist must be prepared to abandon long-cherished theories and laws if exceptions to the theory occur at some future time.

6.Predictions can be inferred from the theory.

All theories tell us something about how things generally work. For example, when Sir Isaac Newton observed an apple falling to the ground, he developed laws of gravity that would apply whenever any object falls; not just apples falling from a particular tree on a particular day. For biologists, good theories, being general and timeless, help us model the future behavior of biological systems. When a theory fails to correctly predict a biological result, then the theory must be either modified or abandoned.

7.A theory must generate new theories.

This last criteria is based on the assumption that everything in the universe has a relationship to one or more other things and that all scientific theories are themselves statements about the relationships among classes of things. Hence, whenever we put forward a new theory, we are always assigning new rules or attributes to one or more classes of objects or assigning new relationships among different classes. By doing so, we raise questions about how new theories change our understanding of the world, and we answer these questions with new hypotheses. These hypotheses can be tested and proven, inspiring the next generation of testable theories. [Glossary Recursive method]

As an example of a biologically proven theory, consider Darwin's theory of evolution by natural selection. Although biological proof never achieves mathematical perfection, the theory of evolution achieves nearly all of the listed criteria for biological proof. Criteria number three (All future observations are consistent with the theory) is a hard nut to crack. So far the theory of evolution has withstood the test of time, and many scientists consider the theory of evolution to be as proven as any biological assertion may be. In the nonmathematical sciences, nothing is really proven, but the burden of trying to prove a theory is, nonetheless, at least as great as anything faced by mathematicians.

Section 1.3 Fallacies

In formal logic courses, we are taught syllogisms as a convenient way to sharpen our thinking skills, but many traditional logic courses focus on the fallacious use of syllogisms, rather than the proper use. The thinking here is that when we study fallacies, we cannot help but learn the requirements for a proper syllogism. For biomedical scientists an understanding of the fundamental laws of reasoning is sufficient to expose and unmask all sorts of fallacious science, and such understanding is often of greater value than the kind of rote memorization that passes for formal medical training. Beyond that, fallacies are fun. [Glossary Biomedicine, Fallacious versus false]

Let's start with a definition. A fallacy is an argument that seems to be logical and true, but is not. The term fallacy carries the connotation that it is intended to deceive, but the fallacies that slip by undetected are often created by sincere