10 How does Science Work?

A Little More Logical | Brendan Shea, PhD

Science is a systematic method of acquiring knowledge about the natural world through observation, experimentation, and logical reasoning. At its core, science seeks to develop explanations for natural phenomena. These explanations, known as scientific theories, are the product of a rigorous process that involves gathering empirical evidence, proposing hypotheses, making testable predictions, and continually refining and updating ideas based on new data. In this chapter, we’ll be taking a detailed look at how the scientific method works.

Learning Outcomes: By the end of this chapter, you will be able to:

• Understand the scientific method and its key components, including observation, hypothesis, prediction, experimentation, and analysis.
• Differentiate between empirical and theoretical hypotheses and recognize their roles in scientific inquiry.
• Explain the concepts of explanandum and explanans and their importance in scientific explanations.
• Evaluate scientific theories using the criteria of adequacy, simplicity, coherence, and fruitfulness.
• Analyze the case study of Darwin’s theory of evolution and Paley’s theory of special creation using the criteria for evaluating scientific theories.
• Compare and contrast the ideas of Rudolf Carnap, Karl Popper, and Thomas Kuhn on the demarcation of science from pseudoscience.
• Apply the principles of scientific reasoning to real-world examples and distinguish between scientific and pseudoscientific claims.

Keywords: Scientific method, Observation, Hypothesis, Prediction, Experimentation, Analysis, Empirical hypothesis, Theoretical hypothesis, Explanandum, Explanans, Adequacy, Simplicity, Coherence, Fruitfulness, Charles Darwin, Theory of evolution, William Paley, Special creation, Rudolf Carnap, Verificationism, Bayesianism, Karl Popper, Falsificationism, Thomas Kuhn, Scientific paradigms, Normal science, Scientific revolutions, Demarcation problem, Pseudoscience

Case Study: A Dialogue About Vaccines

Zephyr and Orchid first met as college roommates at the University of California, Santa Cruz. Despite their different majors – biology for Zephyr and art history for Orchid – they bonded over their shared love of nature documentaries, quirky indie films, and late-night philosophical discussions. They’ve stayed close friends ever since, meeting regularly for coffee and catch-ups.

Today, they’ve met at their favorite café, “The Cosmic Bean.” After chatting about the latest episodes of their favorite podcast, their conversation turns to a more serious topic.

Orchid: Hey Zeph, I’ve been meaning to ask your opinion on something. You know I’ve always been a bit of a hippie at heart, and lately I’ve been reading a lot about alternative medicine. I’ve seen some concerning things about vaccines – stories about kids having serious reactions, even developing autism. It’s got me wondering if they’re really safe.

Zephyr: Oh boy, this takes me back to our late-night dorm discussions! Remember when we got into that heated debate about the ethics of animal testing?

Orchid: How could I forget? We kept our poor neighbors up until 3am!

Zephyr: Good times. But on the vaccine topic – I totally understand your concern. As a scientist, I’ve looked into this a lot. The evidence is really clear that vaccines are safe and effective. They’re one of the greatest public health tools we have.

Orchid: But what about all these stories I’m seeing? Just the other day, I watched a video of a mom testifying about how her child developed autism right after getting the MMR vaccine. It was heartbreaking.

Zephyr: I know, those stories are tough to hear. But we have to be careful about drawing conclusions from individual anecdotes, no matter how compelling they seem. Our brains are wired to see patterns and make connections, even when they might not be real.

Orchid: What do you mean?

Zephyr: Well, just because two events happen close together doesn’t mean one caused the other. It’s like that old superstition about how bad things always happen in threes. Our minds latch onto coincidences and give them meaning.

Orchid: I guess that makes sense. But it’s not just one story – there are whole websites filled with these accounts. And what about that discredited doctor who claimed to have found a link between vaccines and autism?

Zephyr: Ah, you’re talking about Andrew Wakefield. His study was deeply flawed and has been thoroughly debunked. But it got a lot of media attention and scared a lot of people. It’s a great example of why we can’t just rely on one study or one person’s claims.

Orchid: But how do we know which studies to trust? I’ve heard that Big Pharma sometimes suppresses research that goes against their interests.

Zephyr: That’s a valid concern. But there are safeguards in place to protect the integrity of scientific research. Studies are designed to minimize bias, results are peer-reviewed by other experts, and data is made available for reanalysis. The scientific community works hard to correct errors and weed out fraud.

Orchid: Okay, but what about the idea that we should be boosting our immune systems naturally, instead of relying on vaccines? I’ve been reading about homeopathy and herbal remedies that claim to do that.

Zephyr: I know those ideas are appealing, but they’re not supported by scientific evidence. Homeopathy, for example, is based on principles that aren’t compatible with our modern understanding of chemistry and pharmacology.

Orchid: How so?

Zephyr: Homeopathic preparations are often diluted to the point where there may be no molecules of the original substance left. There’s no scientific mechanism for how that could have an effect. It’s like trying to make a cup of coffee by dipping a bean in the ocean!

Orchid: Ha! When you put it that way, it does sound a bit far-fetched. But what about the general idea of supporting immune health through diet and lifestyle?

Zephyr: A healthy lifestyle is certainly important for overall health and immune function. But it’s not a replacement for the specific protection provided by vaccines. Vaccines work by training the immune system to recognize and fight off specific pathogens. They’ve been shown to dramatically reduce the incidence of serious diseases.

Orchid: I see. So, it’s kind of like how eating well and exercising can improve your overall fitness, but you’d still need specific training to run a marathon.

Zephyr: Exactly! That’s a great analogy. And the effectiveness of vaccines is backed up by a huge amount of scientific data. We can see the dramatic decrease in diseases like polio, measles, and pertussis after vaccines were introduced.

Orchid: That’s a good point. I remember learning about the history of polio in one of our classes. It was devastating before the vaccine.

Zephyr: Right. And that’s the power of the scientific method. By carefully testing hypotheses and accumulating evidence, we can develop effective solutions to health problems.

Orchid: This has been really enlightening, Zeph. I appreciate you taking the time to explain the science behind vaccines. But I guess I’m still a little hung up on a few things.

Zephyr: Of course, I’m always happy to discuss further. What’s still bothering you?

Orchid: Well, I’ve been reading about some historical issues with vaccines and other medical treatments. Like how the early smallpox vaccines actually caused a lot of serious side effects and even deaths. And more recently, things like the over-prescription of opioids, antibiotics, and (maybe) statins. It makes me wonder if we might be making similar mistakes with vaccines today that we’re not yet aware of.

Zephyr: Those are valid concerns, and it’s important to learn from history. The early days of medicine were often marked by a lot of trial and error, and some treatments that were once considered safe and effective were later found to be harmful, like the examples you mentioned.

Orchid: Right. So how can we be sure we’re not repeating those mistakes with vaccines?

Zephyr: Well, for one thing, our scientific understanding and safety protocols have advanced enormously since the days of the first smallpox vaccines. Clinical trials for vaccines today are much more rigorous, with multiple phases to test for safety and efficacy, and long-term follow-up to monitor for any rare side effects.

Orchid: But what about the more recent issues, like with opioids? That wasn’t that long ago.

Zephyr: You’re right, and the opioid crisis is a tragic example of how even with modern safeguards, problems can still occur. In that case, a combination of factors including aggressive marketing, changes in prescribing guidelines, and a lack of understanding of addiction risk led to widespread overprescription.

Orchid: So how do we know something similar isn’t happening with vaccines?

Zephyr: It’s a fair question. I think one key difference is that the benefits and risks of vaccines are much more well-established than they were for opioids. We have decades of data showing that vaccines are safe and effective at preventing serious diseases. And unlike pain management, there aren’t really any viable alternatives to vaccination for preventing these diseases on a population level.

Orchid: That makes sense. But I’ve also heard concerns that the profit motive might lead pharmaceutical companies to downplay risks or push for wider use of vaccines than is necessary.

Zephyr: That’s definitely a concern worth taking seriously. There have been instances in the past of drug companies behaving unethically. But I think it’s important to remember that vaccines are highly regulated and scrutinized, not just by the companies that make them but by independent government agencies and the scientific community as a whole.

Orchid: But aren’t those agencies sometimes influenced by industry interests?

Zephyr: It’s true that regulatory capture can be a problem. But in the case of vaccines, there are a lot of different stakeholders involved, from public health agencies to academic researchers to healthcare providers. There’s a strong consensus among experts that the current vaccine recommendations are based on solid science and are in the public’s best interest.

Orchid: I guess that’s reassuring. But I still can’t help feeling a bit uneasy about the profit motive in healthcare.

Zephyr: I hear you, and I think that unease is understandable and even healthy to an extent. We should always be vigilant about potential conflicts of interest and unethical behavior. At the same time, I think it’s important to recognize that the profit motive has also driven a lot of important medical innovations that have saved countless lives.

Orchid: That’s a good point. I suppose it’s about finding the right balance and having strong oversight.

Zephyr: Exactly. And it’s about continually evaluating the evidence and being willing to change course if new data suggests a need for it. Science is always evolving, and there’s always more to learn.

Orchid: That’s a good way to look at it. I guess my takeaway is that while science and medicine aren’t perfect, the process of scientific inquiry is still our best tool for understanding the world and making informed decisions.

Zephyr: I couldn’t agree more. And part of that process is having open, honest discussions like this one, where we can air concerns, look at the evidence, and come to a more nuanced understanding.

Orchid: Well, I’m glad I have you to help me navigate these complex issues. You make a great science translator!

Zephyr: Aw, thanks. And I’m glad I have you to keep me on my toes with your insightful questions!

Orchid: That’s what friends are for, right? To challenge each other and grow together.

Zephyr: Absolutely. Here’s to many more years of friendship, growth, and science!

Discussion Questions

• Zephyr explains that anecdotal evidence, while compelling, isn’t sufficient to draw scientific conclusions. Why is this? What are the limitations of relying on individual stories or experiences when evaluating a scientific claim?
• Orchid expresses unease about the influence of profit motives in healthcare, particularly in the pharmaceutical industry. How can potential conflicts of interest impact scientific research and medical practice? What safeguards are in place to mitigate these conflicts and ensure the integrity of scientific findings?
• Zephyr points out that while science isn’t perfect, it’s a process of continual evaluation and self-correction. How does this self-correcting nature of science work? Can you think of examples where scientific understanding has changed significantly over time based on new evidence?
• The opioid crisis is mentioned as an example of how even with modern safeguards, problems can occur in medical practice. What lessons can be learned from this crisis about the responsible application of scientific knowledge and the need for ongoing monitoring and adjustment of medical guidelines?
• Zephyr emphasizes the importance of looking at the totality of scientific evidence rather than cherry-picking individual studies or anecdotes. Why is it important to consider the broader body of scientific literature when evaluating a claim? How can non-experts assess the strength of scientific consensus on a given issue?

What are Explanations? Explananda and Explanandum

In the context of scientific explanations, the explanandum (plural: explananda) is the phenomenon or fact that needs to be explained. It is the object of the explanation – the thing we are trying to understand or account for.

For example, consider the question “Why do objects fall when dropped?” Here, the explanandum is the fact that objects fall when dropped.

The explanans (plural: explanantia) is the set of statements or propositions that do the explaining. These are the reasons given to account for the explanandum.

In the falling objects example, the explanans might include statements like:

• The object is pulled downward by the force of gravity.
• The force of gravity is proportional to the masses of the objects and inversely proportional to the square of the distance between them.
• There is no force counteracting gravity in this case (like a table surface, air resistance, etc.).

Together, the explanans should provide a satisfactory account of why the explanandum occurs.

Here are a few more examples of explananda and their corresponding explanantia:

Explanandum: Why do airplanes fly?

Explanans:

• Airplanes have wings shaped to create lift by making air move faster over the top of the wing than the bottom.
• The difference in air speed creates a pressure difference, with higher pressure below the wing lifting the plane.
• Engines push the plane forward, allowing the wings to continually generate lift.

Explanandum: Why did the dinosaurs go extinct?

Explanans:

• A massive asteroid or comet impacted the Earth about 66 million years ago.
• The impact triggered global climate changes, including a prolonged period of cooler temperatures and reduced sunlight.
• Many dinosaur species couldn’t adapt to the rapidly changing conditions and died out.

Explanandum: Why do we see phases of the Moon?

Explanans:

• The Moon orbits the Earth approximately once a month.
• The Moon doesn’t produce its own light but reflects sunlight.
• As the Moon orbits, the relative positions of the Sun, Earth, and Moon change, altering how much of the sunlit side of the Moon is visible from Earth.

Explanandum: Why did the car fail to start?

Explanans:

• The car’s battery was drained, unable to power the starter motor.
• The battery drained because the headlights were left on overnight.
• The alternator, which recharges the battery, couldn’t keep up with the power drain from the lights.

Humans spend a lot of our time trying to figure out explanations. This is, in large part, because a good explanation can allow us to change the world in certain ways. For example, we can fix our cars, build an airplane, or send people to the moon.

Empirical and Theoretical Hypotheses

In science, a hypothesis is a tentative explanation for a phenomenon or a proposed answer to a question. It is an educated guess or prediction about how something works, based on prior knowledge and observations. A hypothesis is not merely a wild guess but a reasoned proposal that can be tested through further observation and experimentation.

For example, a scientist studying plant growth might hypothesize that “plants grow taller when given fertilizer.” This hypothesis proposes a relationship between fertilizer (the independent variable) and plant height (the dependent variable).

An empirical hypothesis is a hypothesis whose truth or falsity can be directly observed or measured through experience or experimentation. Empirical hypotheses deal with observable phenomena and can be tested by gathering and analyzing data.

The plant growth hypothesis above is an empirical hypothesis. Its truth can be directly tested by growing plants with and without fertilizer and measuring their heights. Other examples of empirical hypotheses:

• “Objects fall at a constant acceleration due to gravity.”
• “Bacteria will grow more quickly in nutrient-rich environments.”
• “Mice will navigate a maze faster with repeated trials.”

A theoretical hypothesis, in contrast, is a hypothesis about unobservable entities, processes, or relationships. These hypotheses often involve theoretical constructs – abstract ideas or concepts that can’t be directly observed but are inferred from indirect evidence.

Examples of theoretical hypotheses:

• “Electrons orbit the nucleus of an atom in fixed energy levels.”
• “Unconscious desires influence human behavior.”
• “Dark matter accounts for the missing mass in the universe.”

We can’t see electrons, the unconscious mind, or dark matter directly. But we can infer their existence and properties from their observable effects on other phenomena.

Theoretical hypotheses play a central role in many scientific fields. They allow scientists to reason about complex, unobservable processes and develop comprehensive explanations for natural phenomena. Some key examples:

• Fundamental physics. Theoretical entities like quarks, gluons, and the Higgs boson are the foundation of the Standard Model of particle physics.
• Cosmology. Dark matter, dark energy, inflation, and the multiverse are theoretical postulates used to explain the large-scale structure and evolution of the universe.
• Evolution by natural selection. Darwin’s theory of evolution by natural selection is a theoretical explanation for the diversity and adaptations of life on Earth.
• Genetics. Genes, alleles, and other molecular entities are theoretical constructs used to explain patterns of inheritance and variation.
• Germ Theory of Disease. The idea that microscopic pathogens cause many diseases is a theoretical explanation for the spread and treatment of illnesses.
• Macroeconomics. Concepts like supply, demand, elasticity, and market equilibrium are theoretical tools used to model and predict economic behavior.
• Psychology. Constructs like intelligence, personality traits, and mental disorders are theoretical explanations for patterns of human thought and behavior.

Though we can’t observe theoretical entities directly, their explanatory power and predictive success provide strong indirect evidence for their reality. Testing theoretical hypotheses often requires creativity and inference – deriving observable consequences that would follow if the hypothesis were true, then checking if those consequences hold. As theories are refined and updated based on new evidence, our scientific understanding of the world grows deeper and more comprehensive.

How Do We Confirm (or Falsify) Theoretical Hypotheses?

Confirming or falsifying theoretical hypotheses is a central challenge in science. Because theoretical entities and processes can’t be directly observed, scientists must find indirect ways to test their theories. A central way that they do so is by using Arguments to the Best Explanation.

Arguments to the Best Explanation (ABE), also known as abductive reasoning, is a form of inference in which one chooses the hypothesis that would, if true, best explain the relevant evidence.

The basic structure of an ABE is:

• Phenomenon P is observed.
• Hypothesis H1, if true, would explain P.
• No other hypothesis explains P as well as H1 does.
• Therefore, H1 is probably true.

The key idea is that we should infer the truth of the hypothesis that provides the best (i.e., the most satisfactory) explanation of the evidence. What makes an explanation “best” is judged by criteria such as:

• Adequacy: How much of the evidence does the hypothesis explain? Does it account for all the relevant facts?
• Simplicity: Is the hypothesis simple and elegant, or complex and ad hoc? Simpler theories are usually preferred (Occam’s Razor).
• Coherence: Is the hypothesis consistent with our other well-established theories? Does it “fit” with what we already know?
• Fruitfulness: Does the hypothesis open up new avenues for research? Does it suggest new predictions that can be tested?

For example, Einstein’s general theory of relativity predicted that light would bend around massive objects like the Sun. This was a surprising prediction, different from what Newtonian physics predicted.

In the following sections, we’ll take a more detailed look at each of these criteria. We’ll begin by taking a look at the first criterion: Adequacy.

Criteria 1: Adequacy

A key measure of a theory’s strength is its explanatory scope – the range of phenomena it can account for. A theory is more adequate (and thus more likely to be true) if it explains a wider variety of observations.

For example, Darwin’s theory of evolution explains not just the similarities and differences between species, but also the distribution of species across continents, the existence of vestigial organs, the pattern of the fossil record, and much more. Its broad explanatory power is a major reason for its acceptance.

Importantly, adequacy is a comparative notion – theories should be judged against their competitors. A theory might explain a set of observations adequately, but if a rival theory explains those same observations plus additional ones, the rival theory is more adequate.

For example, Copernicus’ heliocentric (“sun-centered”) model of the solar system initially didn’t explain planetary orbits better than Ptolemy’s geocentric (“earth -centered”) model. But once Kepler introduced elliptical orbits, the heliocentric model could explain the detailed motions of the planets while the geocentric model couldn’t, making it more adequate.

When it comes to comparing the adequacy of theories, not all evidence is created equal. Observations that a theory uniquely predicts – especially “risky” or surprising predictions – carry more weight than observations that many theories predict.

If a theory correctly predicts an unexpected phenomenon that other theories can’t account for, that’s strong evidence in its favor. Einstein’s prediction of light bending was a prime example – if light had not bent around the Sun as predicted, general relativity would have been falsified.

Finally, as Karl Popper (more on him later) emphasized, falsifying a theory is logically straightforward – a single contradictory observation is enough. If a theory predicts X, and not-X is observed, the theory is falsified. Confirmation is much harder. No matter how many supporting observations are gathered, the next observation could always falsify the theory. And alternative theories might explain the same evidence. Confirmation is always provisional and a matter of degree.

Therefore, while adequacy is an important criterion for evaluating theories, it must be balanced against other criteria like simplicity, coherence, and fruitfulness (which we’ll explore in the next sections). Scientific theories are never “proven” but are accepted as the best available explanations until new evidence or better theories emerge.

Criteria 2: Simplicity

The second major criterion for evaluating scientific theories is simplicity. Also known as parsimony, this principle states that simpler theories are preferable to more complex ones, all else being equal.

The principle of simplicity is often encapsulated in the maxim known as Occam’s Razor: “Entities should not be multiplied beyond necessity.” Named after the 14th-century philosopher William of Occam, this idea has become a fundamental guiding principle in scientific reasoning.

In science, Occam’s Razor suggests that when multiple competing theories can explain the same phenomena, we should prefer the simplest theory – the one that makes the fewest assumptions and postulates the fewest entities. This is not because simpler theories are necessarily more likely to be true, but because they are easier to test, refine, and work with.

One aspect of simplicity is ontological parsimony– a preference for theories that postulate fewer types of entities or causes. This is related to the principle of methodological naturalism, which holds that scientific explanations should appeal only to natural entities and processes, not supernatural ones.

For example, the theory of evolution is ontologically simpler than creationism because it explains the diversity of life using only natural processes like mutation and selection, without invoking a supernatural designer.

Simplicity also favors theories with fewer “free parameters” – adjustable quantities that can be tuned to fit the data. A theory with many free parameters can often be made to fit any data set, making it less falsifiable and therefore less scientifically useful.

Another aspect of simplicity is pragmatic simplicity – a preference for theories that are easier to understand, apply, and compute with.

For instance, Ptolemaic astronomy, with its complex system of epicycles, could predict planetary motions as accurately as Copernican astronomy for many years. But the Copernican system was much simpler and more intuitive, making it easier for astronomers to work with.

Similarly, Newton’s law of gravity is, strictly speaking, less accurate than Einstein’s general relativity. But Newton’s law is far simpler to apply in most practical situations, so it remains widely used.

Theories as Imperfect “Models”

Scientific theories serve as simplified models of reality, designed to highlight the most critical aspects of phenomena. These models are not exact representations but abstractions that trade off some accuracy for simplicity and ease of use. This concept is succinctly captured in the saying, “All models are wrong, but some are useful.”

Consider the ideal gas law as an example. This law (often taught in high-school science classes, and used in the real world) provides a straightforward model to describe the behavior of gases, expressed by the equation

PV=nRT

In this equation:

• 𝑃 stands for pressure,
• 𝑉 for volume,
• 𝑛 for the number of moles of gas,
• 𝑅 for the gas constant,
• 𝑇 for temperature.

The ideal gas law making the following simplifying assumptions about gas molecules:

• Have perfectly elastic collisions, meaning they bounce off each other without losing energy.
• Have no volume of their own, which means the gas particles are considered points in space.
• Do not exert any forces on each other except during collisions.

These assumptions simplify (and “falsify”) the complex reality of gas behavior, making the law easier to use and understand. Despite these idealizations, the law works well for many real gases under typical conditions.

For greater accuracy, we use more complex models like the van der Waals equation, which adjusts the ideal gas law to account for the volume of gas molecules and the forces between them. The van der Waals equation is:

(P+an2V2​)(V-nb)=nRT

Here:

a and b are constants specific to each gas, reflecting the intermolecular forces and the finite size of molecules, respectively.

While this model is more precise (and “adequate”), it is also more complex, making it harder to work with.

In science, the goal is to strike a balance between simplicity and adequacy. A theory should be as simple as possible, but no simpler—a principle famously articulated by Einstein. Simpler theories are easier to test, understand, and apply. However, they must still be adequate to explain the observed phenomena.

As theories aim to capture more details and become more accurate, they often become more complex. The challenge for scientists is to find the sweet spot where a theory is sufficiently simple to be practical yet detailed enough to accurately describe the complexities of nature.

Criteria 3: External coherence

The third major criterion for evaluating scientific theories is external coherence – how well a theory fits with other established scientific knowledge.

A strong scientific theory should not only explain the phenomena in its own domain but also cohere with theories in other scientific fields. It should “fit” within the broader scientific worldview, not contradict well-established findings in other areas.

This is because science aims to develop a unified, consistent understanding of reality. Theories in different fields should mutually reinforce and constrain each other, forming a coherent web of knowledge.

External coherence matters for several reasons:

• It provides additional indirect support for a theory. If a theory meshes well with other successful theories, that suggests it is on the right track.
• It helps constrain speculation. A theory that contradicts well-established science is less plausible and would require extraordinary evidence to be accepted.
• It guides theory development. Theories are often extended or modified to better cohere with other scientific knowledge.
• It unifies science. Theories that link multiple fields (like the atomic theory, which connects chemistry and physics) are especially valuable.

For example, consider the contrast between two theories related to medicine—“faith healing” and the germ theory of disease.

• Poor coherence: “Faith healing” – the idea that certain illnesses can be cured by religious faith or prayer alone – is incoherent with the germ theory of disease, the effectiveness of modern medicine, and our physiological understanding of the body. Its incoherence with established science is a major reason to doubt its validity.
• Good coherence: The germ theory of disease, in contrast, coheres extremely well with other scientific knowledge. It meshes with cell theory and microbiology (pathogens are microorganisms), epidemiology (diseases spread through exposure), immunology (the body fights infections), and the effectiveness of antiseptics and antibiotics (killing germs cures disease). This coherence is strong evidence for the theory.

Other examples of strongly coherent theories are the atomic theory (which links chemistry and physics), plate tectonics (which integrates geology, seismology, and paleontology), and evolutionary theory (which connects biology with genetics, ecology, and paleontology).

Criteria 4: Fruitfulness

The final major criterion for evaluating scientific theories is fruitfulness or fertility – the ability of a theory to guide future research and open up new areas of inquiry.

A fruitful theory is not a dead end but a stepping stone to further discovery. It raises new questions, suggests new experiments, and guides the development of new technologies.

A fruitful theory:

• Makes novel predictions that can be tested.
• Suggests analogies or connections between different phenomena.
• Opens up new areas of research.
• Guides the development of new instruments or techniques.

In short, a fruitful theory is one that keeps on giving, driving scientific progress forward.

For example:

• Evolutionary theory has been enormously fruitful, leading to new fields like population genetics, inspiring new questions about the mechanisms of inheritance, and guiding research into the history of life on Earth.
• Quantum theory, similarly, has led to a vast array of new research into the subatomic world, the development of new technologies like lasers and semiconductors, and new theoretical frontiers like quantum computing and quantum gravity.
• The theory of plate tectonics opened up new research into the history of Earth’s continents, the causes of earthquakes and volcanoes, and the evolution of climate over geological time.

Fruitfulness is arguably the most important criterion for scientific theories in the long run. Theories that are adequate, simple, and coherent but fail to generate new research will eventually stagnate. But fruitful theories, even if initially limited or incomplete, will continue to drive science forward, attracting more research and evolving over time.

Together, these four criteria – adequacy, simplicity, coherence, and fruitfulness – provide a comprehensive framework for evaluating the strength of scientific theories. Theories that excel on all four criteria, like evolution or quantum mechanics, form the bedrock of our scientific understanding of the world. But even the best theories are always provisional, subject to revision or replacement as new evidence emerges. The ongoing quest to develop ever more adequate, simple, coherent, and fruitful theories is the essence of the scientific enterprise.

Extended Example: Darwin and Paley

In the history of biology, two figures stand out for their contrasting explanations of the diversity and adaptations of life on Earth: William Paley and Charles Darwin. Their theories – Paley’s theory of special creation and Darwin’s theory of evolution by natural selection – offer a compelling case study in the application of the criteria for evaluating scientific hypotheses.

William Paley and the Theory of Special Creation

William Paley (1743-1805) was an English clergyman, philosopher, and early proponent of the argument from design. In his book “Natural Theology” (1802), Paley laid out his theory of special creation.

Paley argued that the complex design of living things, particularly their intricate adaptations to their environments, could only be explained by the existence of a divine Creator. Just as a complex artifact like a watch implies the existence of a watchmaker, Paley reasoned, the even greater complexity of living organisms implies the existence of a divine Designer.

According to Paley’s theory, each species was separately created by God in its current form, perfectly adapted to its environment. Species were thought to be fixed and unchanging, with no evolutionary relationship to each other.

Charles Darwin and the Theory of Evolution by Natural Selection

Charles Darwin (1809-1882) was an English naturalist whose theory of evolution by natural selection revolutionized our understanding of the living world.

In his book “On the Origin of Species” (1859), Darwin proposed that the diversity of life is the result of a gradual, natural process of change over vast periods of time. New species arise through the mechanism of natural selection.

According to Darwin’s theory:

• Organisms within a population show variation in their traits.
• Some of these variations are inheritable.
• In each generation, more offspring are produced than can survive, given limited resources.
• Organisms with traits that are advantageous in the current environment are more likely to survive and reproduce. (This is called selection.)
• Over many generations, this differential survival and reproduction can lead to significant changes in populations, eventually resulting in the emergence of new species.

Darwin’s theory provided a natural explanation for the adaptations of organisms, the diversity of species, and the patterns of similarity and difference among species. It depicted the history of life as a branching tree, with all species related through common descent, rather than as separate, special creations.

In the next sections, we will examine the evidence for each theory and see how they stack up against our criteria for evaluating scientific hypotheses – adequacy, simplicity, coherence, and fruitfulness. Through this case study, we’ll see how Darwin’s theory, by better meeting these criteria, led to a profound shift in our understanding of the living world and our place in it.

Adequacy: Evolution versus Special Creation

Let’s begin by considering the explanatory adequacy of both theories. In The Origin of Species, Darwin offered a number of different arguments that bear on this criteria.

Comparative Anatomy and Embryology

One area where Darwin’s theory excels is in explaining the patterns of similarity and difference among species.

Comparative anatomy reveals striking similarities in the structure of different species’ bones, organs, and other body parts. For example, the forelimbs of mammals like bats, whales, horses, and humans have the same basic bone structure, despite serving very different functions (flying, swimming, running, grasping).

Embryology, the study of how organisms develop, also reveals unexpected similarities. The early embryos of fish, reptiles, birds, and mammals look remarkably alike, with gill slits, tails, and similar arrangements of organs. As they develop, these embryos diverge to their adult forms.

Darwin’s theory explains these similarities as the result of common descent. Related species share similar structures because they inherited them from a common ancestor. The similarities in embryos reflect shared ancestral developmental patterns. Differences arise through gradual divergence as species adapt to different environments.

Paley’s theory, in contrast, struggles to explain these patterns. If each species was separately created for its environment, why do they share such similar structures? Why would a whale have the same forelimb bones as a bat? Special creation suggests that similarities between species are coincidental or reflect the whims of the Creator, rather than any deeper relationship.

Biogeography

Darwin’s theory also explains the patterns of species distribution across the globe, known as biogeography.

Darwin observed that species tend to be most closely related to other species in the same geographic area. For example, the species of finches on the Galapagos Islands are more similar to each other than to finches elsewhere in the world. Similarly, the marsupials of Australia are distinct from the mammals of other continents, but closely related to each other.

Darwin’s theory explains these patterns as the result of species spreading to new areas, then evolving in isolation. As populations are separated by geographic barriers, they diverge over time into distinct species through natural selection and other evolutionary processes.

Paley’s theory, again, has difficulty accounting for these patterns. If species were separately created for their environments, why do we find such strong geographic clustering of related species? Special creation suggests species distributions should be more random or reflect the specific environments for which they were designed.

Fossil Record

The fossil record provides a direct window into the history of life on Earth. It reveals a sequence of organisms that have existed over geological time, with earlier organisms generally being simpler and more primitive than later ones.

Darwin’s theory predicts this pattern. It suggests that life began with simple forms and gradually became more complex over time through the accumulation of evolutionary changes. It also predicts the existence of transitional forms between major groups of organisms, as species evolve from one form to another.

While the fossil record is incomplete and has gaps, many transitional forms have been discovered since Darwin’s time, such as the early birds Archaeopteryx (transitional between reptiles and birds) and the walking whales Ambulocetus (transitional between land mammals and whales).

Paley’s theory, in contrast, predicts that all species should appear in the fossil record fully formed, without transitional stages. It also suggests that the ordering of fossils should reflect the separate creation of species, rather than any evolutionary progression. The actual patterns in the fossil record are difficult to reconcile with special creation.

Vestigial Structures

Many organisms possess vestigial structures – reduced or non-functional versions of organs or body parts that were more fully developed and functional in ancestral species. Examples include the tiny leg bones of whales, the wings of flightless birds like ostriches, and the human appendix.

Darwin’s theory explains vestigial structures as remnants of ancestral traits that have lost their function due to changes in the environment or way of life. As a species evolves, structures that were once useful may become less so, and may be reduced or lost over generations if the cost of maintaining them outweighs any remaining benefit.

Paley’s theory has no easy explanation for vestigial structures. If organisms were specially created for their current environments, why would they retain non-functional remnants of structures that are useful in other environments? Special creation suggests that vestigial structures are inexplicable, or even detrimental, design flaws.

In each of these areas – comparative anatomy, embryology, biogeography, the fossil record, and vestigial structures – Darwin’s theory provides a more adequate explanation than Paley’s. It accounts for a wide range of observations that are difficult to reconcile with special creation, using a few simple principles like common descent, natural selection, and gradual change over time.

This explanatory power was a major reason for the acceptance of Darwin’s theory by the scientific community, despite initial resistance. While special creation could account for the adaptations of organisms to their environments, it struggled to explain the deeper patterns of similarity, difference, and historical succession that Darwin’s theory elegantly tied together.

Evolution vs Special Creation: Simplicity

In terms of simplicity, Darwin’s theory has a clear advantage over Paley’s. Recall that simplicity in science refers to explaining phenomena with the fewest assumptions and postulated entities.

Darwin’s theory explains the diversity and adaptations of life using a few simple principles:

• Organisms vary in their inherited traits.
• Organisms produce more offspring than can survive and reproduce, leading to a struggle for existence.
• Organisms with traits that are advantageous in the current environment are more likely to survive and pass on their traits.
• Over long periods of time, this process of natural selection leads to the accumulation of adaptations and the emergence of new species.

With these principles, Darwin was able to account for a vast array of biological phenomena, from the fit of organisms to their environments to the patterns of similarity and difference among species.

Paley’s theory, in contrast, requires a separate assumption for each species – that it was specially created by God for its environment. This multiplies assumptions and explanatory entities (God’s creative acts) far beyond necessity.

Furthermore, Paley’s theory requires additional assumptions to explain away the evidence for evolution, such as the similarities between species, the patterns in the fossil record, and the existence of vestigial structures. Each of these requires a separate ad hoc explanation (e.g., that God created species with similar structures for unknown reasons, or that the fossil record is deceptive).

In this way, Darwin’s theory is much simpler than Paley’s. It explains more with less, providing a more parsimonious account of the biological world.

Evolution vs Special Creation: Coherence

Darwin’s theory also demonstrates superior external coherence – it fits better with established knowledge from other scientific fields.

One area of coherence is with geology. By the time of Darwin, geologists like Charles Lyell had established that the Earth was far older than the few thousand years suggested by a literal reading of the Bible. This vast age was necessary for the slow process of evolution by natural selection to produce the diversity of life we see today. Paley’s theory, rooted in a literal Biblical chronology, was in tension with this geological knowledge.

Darwin’s theory also cohered with the emerging science of genetics. While Darwin himself did not know the mechanism of inheritance, his theory required that traits be inheritable and variable. The rediscovery of Gregor Mendel’s work on genetics in the early 20th century provided the missing piece of Darwin’s puzzle, explaining how traits are inherited and how variation arises through mutation and recombination. This synthesis of evolution and genetics, known as the “modern synthesis,” further strengthened the coherence of evolutionary theory.

In contrast, Paley’s theory stood apart from these scientific developments. Special creation did not require an old Earth or a mechanism of inheritance, and made no predictions about these areas that could be confirmed or refuted. It was isolated from the broader scientific framework.

Darwin’s theory also cohered with the growing understanding of the place of humans in nature. Anatomical and fossil evidence was already suggesting that humans were closely related to other primates. Darwin’s theory provided an explanation for this relationship, showing how humans could have evolved from a common ancestor with other apes. Paley’s theory, with its separate creation of humans in the image of God, was challenged by this evidence.

Over time, Darwin’s theory has become even more deeply integrated with other scientific fields. It is now the unifying framework for all of biology, from molecular genetics to ecology. It has also made fruitful connections with fields as diverse as computer science (genetic algorithms), medicine (antibiotic resistance), and psychology (evolutionary psychology).

This deep coherence with established and emerging scientific knowledge is a strong mark in favor of Darwin’s theory. Science aims for a unified, mutually reinforcing understanding of the world, and theories that connect and cohere with other successful theories are more likely to be true than those that remain isolated.

Paley’s theory, rooted in a pre-scientific worldview and resistant to integration with new scientific discoveries, fares poorly on this criterion. Its lack of coherence with the rest of science is a major reason for its rejection by the scientific community.

In the next section, we will examine the final criterion – fruitfulness. We will see how Darwin’s theory has been an incredibly fertile source of new research questions and discoveries, while Paley’s has led to few, if any, scientific advances. This contrast will complete our case study, demonstrating the power of scientific reasoning to adjudicate between competing hypotheses and to guide us toward a deeper understanding of the natural world.

Evolution vs Special Creation: Fruitfulness

Perhaps the most striking difference between Darwin’s and Paley’s theories is in their scientific fruitfulness – their ability to inspire new research questions, guide new discoveries, and open up new areas of inquiry.

Darwin’s theory has been incredibly fertile in this regard. It has inspired over 150 years of productive research in a wide range of biological fields, from paleontology to molecular biology. Here are just a few examples of the research programs and discoveries that have been guided by evolutionary theory:

• Comparative genomics: By comparing the genomes of different species, researchers have been able to reconstruct the evolutionary history of genes and organisms, identify the genetic basis of adaptations, and even predict the functions of previously unknown genes.
• Experimental evolution: By exposing populations of organisms to controlled selective pressures in the lab, researchers have been able to directly observe evolutionary processes in real-time, testing and refining the principles of evolutionary theory.
• Evolutionary developmental biology (evo-devo): By studying how developmental processes evolve, researchers have gained new insights into how changes in gene regulation can give rise to new morphological features and how developmental constraints can shape the path of evolution.
• Evolutionary medicine: By applying evolutionary principles to the study of disease, researchers have developed new approaches to understanding the origins and spread of pathogens, the evolution of antibiotic resistance, and the role of evolutionary mismatch in chronic diseases.
• Evolutionary psychology: By viewing the human mind as a product of evolution, researchers have generated new hypotheses about the adaptive functions of psychological traits, the origins of social behaviors, and the roots of cognitive biases.

These are just a few examples – evolutionary theory has also had a profound influence on fields as diverse as artificial intelligence, conservation biology, and anthropology. The theory’s ability to generate new, testable predictions and to open up new areas of research is a testament to its scientific power and validity.

In contrast, Paley’s theory has been largely scientifically sterile. Because it invokes supernatural causation and is not amenable to empirical test, it has not generated a progressive research program. Once one has said “God did it,” there is little more to investigate or discover.

Special creation did not lead to new predictions or discoveries in comparative anatomy, embryology, biogeography, or any of the other fields where evolutionary theory has been so fruitful. It did not guide researchers to new questions or areas of inquiry. At best, it served as a science-stopper, an explanation that precluded further investigation.

This contrast in fruitfulness is perhaps the most consequential difference between the two theories. Science progresses by generating new knowledge, and theories that are more productive in this regard are more valuable, even if they are not perfect.

Discussion Questions

• What are the key differences between Darwin’s theory of evolution and Paley’s theory of special creation?
• How does Darwin’s theory better explain the patterns of similarity and difference among species compared to Paley’s theory?
• In what ways is Darwin’s theory simpler than Paley’s? Why is simplicity an important criterion for evaluating scientific theories?
• How does Darwin’s theory cohere with knowledge from other scientific fields like geology and genetics? Why is coherence important in science?
• What are some examples of how Darwin’s theory has been scientifically fruitful? Why is fruitfulness a key marker of a successful scientific theory?
• Why has Paley’s theory been less scientifically productive than Darwin’s? What does this suggest about the value of supernatural explanations in science?
• How has the evidence for evolution grown since Darwin’s time? What new fields and discoveries have emerged from evolutionary theory?
• Are there any phenomena in biology that Darwin’s theory struggles to explain? How do scientists approach these challenges to evolutionary theory?
• How has the study of evolution influenced other fields beyond biology? What are some examples of the broader impact of evolutionary thinking?
• What do you think are the most compelling reasons to accept Darwin’s theory over Paley’s? Which criteria for evaluating theories do you think are most important?

Philosophy of Science: Distinguishing Science from Pseudoscience

One of the key questions in the philosophy of science is how to demarcate science from non-science or pseudoscience. This problem was at the heart of the debate between Darwin’s theory of evolution and Paley’s theory of special creation, and it remains relevant today in discussions of topics like intelligent design, psychoanalysis, and astrology.

Several prominent philosophers of science have proposed criteria for distinguishing scientific theories from non-scientific or pseudoscientific ones. Let’s examine the ideas of Rudolf Carnap, Karl Popper, and Thomas Kuhn.

Rudolf Carnap: From Verificationism to Bayesianism

Rudolf Carnap was a key figure in the Vienna Circle, a group of philosophers who advanced logical positivism. Logical positivism is a philosophical movement that focuses on the use of logic and scientific methods to analyze language and knowledge. One of its central tenets was verificationism, the idea that the meaning of a statement is determined by its method of verification.

Initially, Carnap and his colleagues believed that a statement is meaningful only if it can be empirically verified—that is, confirmed or disconfirmed through observation or experiment. According to this principle:

• Empirical Verification: A statement must be testable against observable reality to be considered meaningful. For example, “Water boils at 100°C at sea level” is a meaningful statement because it can be verified through experimentation.
• Meaninglessness of Non-Empirical Statements: Statements that cannot be empirically verified, such as those in metaphysics or theology, are deemed meaningless. For instance, “God exists” is considered meaningless in this context because it cannot be tested through empirical means.

Over time, though, Carnap recognized the limitations of verificationism. He realized that many scientific theories cannot be conclusively verified or falsified but can still be highly useful and informative. (This include Darwin’s theory!) This led him to adopt a more nuanced view, grounded in Bayesianism.

Bayesianism is a probabilistic approach to scientific reasoning that uses evidence to update the likelihood of hypotheses. Rather than seeking absolute verification, Bayesianism evaluates how evidence changes the probability of a theory being correct. In this framework, the following two things are what matters:

Prior Probability: Scientists begin with an initial probability (the prior) for a hypothesis based on existing knowledge or assumptions.

Updating with Evidence: As new data emerges, the prior probability is updated using Bayes’ Theorem, which calculates the likelihood of the hypothesis given the new evidence.

Bayes’ Theorem is expressed as:

PHE=PEH⋅PHP(E)

Where:

𝑃(𝐻∣𝐸)is the posterior probability of the hypothesis 𝐻H given the evidence 𝐸E.

𝑃(𝐸∣𝐻) is the likelihood of observing the evidence 𝐸E if the hypothesis 𝐻H is true.

𝑃(𝐻) is the prior probability of the hypothesis.

𝑃(𝐸) is the probability of the evidence under all possible hypotheses.

According to Bayesianism, scientific theories are never absolutely confirmed or disconfirmed. Instead, they are assigned probabilities that are continually updated as new evidence becomes available.

From Carnap’s later perspective, the scientific validity of a theory is determined by its ability to make empirically verifiable predictions and to be updated in light of new evidence. Consider the debate between Darwin’s theory of evolution and Paley’s theory of intelligent design:

• Darwin’s Theory of Evolution: This theory makes specific predictions about biological phenomena, such as the patterns of similarity and difference among species and the existence of transitional fossils. These predictions can be empirically verified, thus providing evidence that can update the probability of the theory being correct. For example, finding a transitional fossil that fits evolutionary predictions increases the posterior probability of Darwin’s theory.
• Paley’s Theory of Intelligent Design: This theory makes no surprising claims about the biological phenomena and is if fact compatible with all possible observations. Since these claims cannot be tested or observed, they do not provide a basis for probabilistic updates. Therefore, from a Bayesian perspective, Paley’s theory lacks the empirical grounding necessary for scientific credibility.

In short, Carnap would explain scientist’s preference for Darwin’s theory over Paley’s theory by noting that Darwin’s theory is more likely to be true, given our available evidence. Carnap’s approach has been taken by modern “Bayesians”, and remains a prominent idea in philosophy, computer science, statistics, and other areas.

Karl Popper and Falsificationism

While Rudolf Carnap shifted from verificationism to Bayesianism, Karl Popper offered a different approach to demarcate science from pseudoscience. Popper’s philosophy of science focused on falsificationism, a method that emphasizes the role of deduction over induction in scientific inquiry.

Popper’s criticism of Carnap’s approach is grounded in his worries about induction. Here, induction is the process of deriving general principles from specific observations. For example, observing that the sun rises every morning and concluding that it will always rise is an inductive inference. However, David Hume pointed out a fundamental problem with induction: no matter how many observations we make, we can never be certain that future observations will follow the same pattern. This problem undermines the certainty of scientific knowledge if it relies solely on induction.

Because of this problem, Popper argued that science should rely on deduction, where conclusions follow logically from premises. Instead of trying to verify theories through induction, Popper proposed that scientists should focus on falsifiability—the capacity of a theory to be tested and potentially proven false.

Falsificationism is the idea that a scientific theory should be structured in such a way that it can be rigorously tested and potentially falsified by empirical evidence. According to Popper:

• Falsifiability as a Criterion: A theory is scientific if and only if it is falsifiable. For example, the theory “All swans are white” is scientific because it can be falsified by observing a single black swan.
• Degree of Falsifiability: The more a theory exposes itself to potential falsification, the more scientific it is. A theory that makes bold predictions that can be easily tested is considered highly falsifiable.

Popper used falsifiability to distinguish between science and pseudoscience:

• Scientific Theories: These are theories that make bold predictions that can be rigorously tested and potentially falsified. For example, Darwin’s theory of evolution makes specific, testable predictions about fossil records and genetic similarities among species.
• Pseudoscientific Theories: These are theories that are structured in a way that makes them immune to falsification. For example, Paley’s theory of intelligent design relies on supernatural explanations that cannot be empirically tested or falsified.

More generally, Popper viewed the process of scientific discovery itself as an evolutionary process:

• Conjectures and Refutations: Scientists propose hypotheses (conjectures) and then attempt to refute them through rigorous testing. Theories that withstand falsification are tentatively accepted but always remain open to further testing and potential refutation.
• Survival of the Fittest Theories: Similar to natural selection, where organisms best adapted to their environment survive, the most robust scientific theories are those that survive repeated attempts at falsification.

So: Karl Popper’s falsificationism offers a deductive alternative to Carnap’s probabilistic approach, emphasizing the critical role of testability and the potential for refutation in scientific inquiry. By focusing on the degree of falsifiability, Popper provides a robust criterion for distinguishing science from pseudoscience, framing scientific progress as an evolutionary process of conjecture and refutation.

Thomas Kuhn: Paradigms and Normal Science

Thomas Kuhn offered a different perspective on the nature of science than either Carnap or Popper. He argued that science progresses through periods of “normal science,” punctuated by occasional “scientific revolutions.”

A paradigm is a comprehensive model or pattern of knowledge that defines a scientific discipline during a particular period. It encompasses the accepted theories, methods, standards, and assumptions that guide researchers in their work. According to Kuhn, scientific progress involves the following stages:

• Normal Science: This is the regular work of scientists theorizing, observing, and experimenting within the current paradigm. Scientists engage in problem-solving activities, known as “puzzle-solving,” using the established framework. For example, in evolutionary biology, researchers study genetic variation, natural selection, and adaptation based on Darwinian principles.
• Anomalies: During the course of normal science, scientists encounter anomalies—observations or problems that cannot be easily explained by the current paradigm. For instance, before the discovery of DNA’s structure, the mechanisms of genetic inheritance were poorly understood, posing challenges to the prevailing biological theories.
• Crisis: As anomalies accumulate and become more problematic, a crisis can occur. This crisis signifies a growing recognition that the current paradigm may be inadequate to explain certain phenomena. For example, the pre-Darwinian understanding of species creation faced mounting evidence from paleontology and comparative anatomy that suggested a different explanation.
• Scientific Revolution: When the crisis reaches a tipping point, a scientific revolution may occur. This period of revolutionary science involves the abandonment of the old paradigm and the adoption of a new one that better explains the anomalies. The transition from pre-Darwinian biology to Darwin’s theory of evolution is a prime example of such a revolution.
• New Paradigm: The new paradigm provides a fresh framework for normal science, guiding future research and problem-solving. For instance, the paradigm shift brought about by Darwin’s theory established a new foundation for understanding biological diversity and the processes of evolution.

Evolutionary biology serves as an exemplary scientific paradigm, in the following ways:

• It offers a unified explanation for the diversity of life, the mechanisms of genetic inheritance, and the adaptation of species to their environments.
• Evolutionary biology makes specific, testable predictions. For instance, it predicts the existence of transitional fossils and the patterns of genetic similarity among related species. These predictions can be confirmed or disconfirmed through observation and experimentation.
• The paradigm of evolutionary biology guides scientific research, helping scientists formulate hypotheses, design experiments, and interpret data.

In contrast, special creation lacks the characteristics of a scientific paradigm:

• It relies on supernatural explanations that don’t serve to define any specific methods of research. (For example, there is a notable lack of mathematical “laws”, agreement on important outstanding problems, accepted methods of investigation, etc.).
• Special creation does not make specific, testable predictions about biological phenomena.
• Without a guiding framework for empirical research, special creation does not foster the cumulative advancement of knowledge.

In the end, Thomas Kuhn’s theory of scientific paradigms helps explain why evolutionary biology constitutes a robust scientific field. It provides a comprehensive, empirically testable framework that guides research and fosters the progressive accumulation of knowledge. Special creation, lacking these qualities, does not meet the criteria of a scientific paradigm.

Conclusion

The ideas of Carnap, Popper, and Kuhn offer different perspectives on the problem of demarcating science from pseudoscience. Carnap emphasizes confirmation by surprising predictions, Popper stresses falsifiability, and Kuhn focuses on the puzzle-solving characteristic of normal science.

While these criteria differ in their specifics, they converge on some common themes. Science, at its best, is empirical (based on observational evidence), testable (making predictions that can be confirmed or disconfirmed), and progressive (leading to the accumulation of knowledge and the solving of problems).

Pseudoscience, in contrast, often relies on unverifiable claims, makes few testable predictions, and fails to progress or generate new knowledge.

The case study of Darwin and Paley illustrates these themes. Darwin’s theory, while not perfect, exhibits many of the hallmarks of good science: it is based on extensive empirical evidence, makes many bold and falsifiable predictions, and has led to a progressive research program. Paley’s theory, while initially intuitive, ultimately fails as a scientific explanation: it relies on unverifiable supernatural causes, makes few testable predictions, and has not led to new discoveries or solved problems.

Of course, the demarcation problem remains a complex and contested issue in the philosophy of science. The criteria proposed by Carnap, Popper, and Kuhn have all been critiqued and refined by subsequent thinkers. And the boundaries between science and non-science are not always sharp, with many theories falling somewhere on a continuum.

Nonetheless, these philosophical ideas provide useful tools for critically evaluating claims and theories that purport to be scientific. By demanding empirical support, testable predictions, and progressive problem-solving, we can separate genuine scientific advances from pseudoscientific impostors, and continue the quest for reliable knowledge about the natural world that animates all of science.

Discussion Questions

• How does Carnap’s shift from verificationism to Bayesianism reflect the evolving understanding of scientific reasoning? What are the strengths and limitations of each approach?
• In what ways does Popper’s falsificationism address the problem of induction raised by Hume? How does focusing on falsifiability help demarcate science from pseudoscience?
• Kuhn’s theory of scientific paradigms suggests that science progresses through periods of normal science punctuated by revolutions. How does this view differ from the more linear, cumulative view of scientific progress often presented in textbooks?
• Considering the criteria proposed by Carnap, Popper, and Kuhn, which do you think is most effective in distinguishing science from pseudoscience? Why?
• How does the case study of Darwin’s theory of evolution and Paley’s theory of special creation illustrate the differences between science and pseudoscience?
• Can you think of any contemporary examples of theories or claims that straddle the line between science and pseudoscience? How might the ideas of Carnap, Popper, and Kuhn help us evaluate these cases?
• The text suggests that genuine science is empirical, testable, and progressive. Are there any other characteristics you would add to this list? Are there any exceptions to these criteria?
• How might the demarcation criteria proposed by these philosophers apply to fields outside the natural sciences, such as social sciences or humanities? Are there any limitations to applying these criteria more broadly?
• The text acknowledges that the boundaries between science and non-science are not always clear-cut. How should we approach theories or claims that fall into this gray area? What other factors might we consider in evaluating their scientific merit?
• Reflecting on the ideas presented, how has your understanding of the nature of science and pseudoscience changed? What insights or questions do you still have about the demarcation problem in the philosophy of science?

Glossary

 

Term	Definition
(Empirical) Adequacy	The ability of a theory to explain a wide range of phenomena in its domain
Anomaly (Kuhn)	A puzzle or observation that cannot be adequately explained within the current paradigm, potentially leading to a crisis and a paradigm shift
Bayesian Epistemology (Carnap)	An approach to inductive logic and scientific reasoning based on updating probabilities of hypotheses in light of new evidence, using Bayes’ theorem
Charles Darwin	English naturalist who proposed the theory of evolution by natural selection
Confirmation	The act of supporting or strengthening a hypothesis or theory through empirical evidence, while recognizing that conclusive proof is impossible in science
Empirical Hypothesis	A hypothesis whose truth or falsity can be directly observed or measured through experience or experimentation
Evolution by Natural Selection	The process by which species change over time, with new species arising from pre-existing species through the mechanisms of variation, differential survival and reproduction, and heredity
Evolutionary Account of Theory Choice (Popper)	The idea that the selection of scientific theories occurs through a process analogous to natural selection, with theories that better survive empirical tests and solve problems being favored
Explanation (Explanandum/Explanans)	The fact or phenomenon to be explained / The statements or propositions that do the explaining
External Coherence	The consistency of a theory with established knowledge from other scientific fields
Falsifiability (Popper)	The principle that a scientific theory must make testable predictions that could potentially be proven false by empirical evidence
Falsification	The act of disproving a hypothesis or theory through empirical observation or experimentation
Fruitfulness	The ability of a theory to generate new research questions, guide new discoveries, and open up new areas of inquiry
Ideal Gas Law	A simple but powerful model of gas behavior that assumes perfectly elastic collisions and neglects intermolecular forces
Inference to the Best Explanation	A form of abductive reasoning in which one chooses the hypothesis that would, if true, best explain the relevant evidence
Karl Popper	Austrian-British philosopher who proposed the criterion of falsifiability for distinguishing science from non-science
Methodological Naturalism	The principle that scientific explanations should appeal only to natural entities and processes, not supernatural ones
Model	A simplified representation of a complex system or phenomenon, often involving idealizations and abstractions
Normal Science (Kuhn)	The day-to-day work of scientists in solving puzzles and accumulating knowledge within a shared paradigm
Occam’s Razor	The principle that entities should not be multiplied beyond necessity; a preference for simpler theories
Paradigm (Kuhn)	A set of shared assumptions, methods, and exemplars that guide research in a scientific field during a period of normal science
Pragmatic Simplicity	A preference for theories that are easier to understand, apply, and compute with
Pseudoscience (Popper)	A theory or practice that claims to be scientific but fails to meet the criteria of falsifiability and other hallmarks of genuine science
Revolutionary Science (Kuhn)	The process by which an old paradigm is replaced by a new one that can better account for anomalies and guide future research, often involving a fundamental shift in assumptions and methods
Rudolph Carnap	German-American philosopher who was a leading member of the Vienna Circle and a proponent of logical positivism
Simplifying Assumption	An assumption made to make a model or theory more tractable, even if it is known to be false in some respects
Theoretical Hypothesis	A hypothesis about unobservable entities, processes, or relationships, often involving theoretical constructs
Theory of Special Creation	The idea that each species was separately created by God in its current form
Thomas Kuhn	American philosopher and historian of science who introduced the concept of paradigms and scientific revolutions
Verifiability (Carnap)	The idea that the meaning of a statement consists in its method of empirical verification
William Paley	English clergyman and philosopher who proposed the theory of special creation and the argument from design

 

References

• Bird, Alexander. 2021. “Thomas Kuhn.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta. https://plato.stanford.edu/entries/thomas-kuhn/.
• Darwin, Charles. 1859. On the Origin of Species by Means of Natural Selection. London: John Murray. https://www.gutenberg.org/files/1228/1228-h/1228-h.htm.
• Godfrey-Smith, Peter. 2003. Theory and Reality: An Introduction to the Philosophy of Science. Chicago: University of Chicago Press.
• Hájek, Alan. 2019. “Interpretations of Probability.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta. https://plato.stanford.edu/entries/probability-interpret/.
• Hempel, Carl G. 1965. “Studies in the Logic of Confirmation.” In Aspects of Scientific Explanation and Other Essays in the Philosophy of Science, 3-51. New York: Free Press.
• Kuhn, Thomas S. 1962. The Structure of Scientific Revolutions. Chicago: University of Chicago Press.
• Okasha, Samir. 2016. Philosophy of Science: Very Short Introduction. 2nd ed. Oxford: Oxford University Press.
• Paley, William. 1802. Natural Theology or Evidences of the Existence and Attributes of the Deity. London: R. Faulder. https://www.gutenberg.org/ebooks/14780
• Popper, Karl. 1959. The Logic of Scientific Discovery. London: Hutchinson.
• Popper, Karl. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge.
• Salmon, Wesley C. 1984. Scientific Explanation and the Causal Structure of the World. Princeton: Princeton University Press.
• Shea, Brendan. 2018. “Paradigm Shift: A ‘Strange’ Case of a Scientific Revolution.” In: W. Irwin and M. White (eds), Dr. Strange and Philosophy: The Other Book of Forbidden Knowledge. The Blackwell Series in Popular Culture and Philosophy. (Hoboken: Wiley), 139-150.
• Shea, Brendan. 2016. “Karl Popper: Philosophy of Science.” The Internet Encyclopedia of Philosophy, ISSN 2161-0002, http://www.iep.utm.edu/pop-sci/ .
• Thornton, Stephen. 2021. “Karl Popper.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta. https://plato.stanford.edu/entries/popper/.
• Vickers, John. 2016. “The Problem of Induction.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta. https://plato.stanford.edu/entries/induction-problem/.
• Woodward, James. 2019. “Scientific Explanation.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta. https://plato.stanford.edu/entries/scientific-explanation/.
• Understanding Science. 2023. University of California, Berkeley. https://undsci.berkeley.edu/.
• The Scientific Method. 2023. Khan Academy. https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/the-science-of-biology.