Lecture 02: The Process of Science

Administrative Details

  • Bio 131 (lab): Starts next week.
  • Textbook quiz 1: Due on Sunday.
  • "Qwickly" attendance: Starts on Monday.
  • Mastering Biology: Instructions for setup are at the bottom of the Canvas homepage.
  • Instructor Contact: bwinslow@umassd.edu, Office hours M/W/F 10-11, Tu 1-2.

Introduction to Science

  • Main Points of the Lecture:
    • The philosophy and logic underpinning scientific inquiry.
    • Fundamental rules that govern how science is conducted.
    • The requirement for theories to allow for testable predictions.
    • Scientific arguments must be based on facts.
    • Crucially, hypotheses and theories cannot be definitively proven, though they can be refuted and overturned.

What is Science?

  • Science is fundamentally a branch of philosophy.
  • It is an organized, systematic search for information, explanations, and answers to specific questions about the natural world.
  • Science employs two primary forms of reasoning:
    • Inductive Reasoning: Often associated with descriptive science.
    • Deductive Reasoning: Often associated with experimental science.

Fundamental Logic in the Philosophy of Science

  • Facts:
    • Represent data, measurements, and observations about the natural world.
    • They are empirical: you are encouraged to "see for yourself."
    • Facts are considered true and can generally be proven.
    • Philosophical Nuance: Many philosophers contend that even facts cannot be truly proven in an absolute sense, highlighting inherent limitations in human knowledge and perception.
  • Hypothesis:
    • A potential explanation for a specific phenomenon.
    • Must explain existing facts and be formulated in a way that allows it to be tested through experiments.
    • A hypothesis cannot be proven to be absolutely true.
  • Theory:
    • A hypothesis that has been exceptionally well-tested and is widely supported by a vast body of empirical evidence.
    • In science, "theory" holds a much more rigorous and significant meaning than its everyday usage (where it might imply a mere guess or speculation).
  • Karl Popper's Insight: A key philosophical principle in science is that a hypothesis can never be definitively proven true (because alternative explanations might always exist or new evidence could emerge). However, a hypothesis can be falsified or refuted by evidence.

Key Terms and Relationships

  • Facts: Things we know are true; a statement of a phenomenon.
  • Hypotheses: Explanations for facts; typically phrased as a "phenomenon/because" statement (XX happens because of YY). While they can be strongly supported by facts, they can never be definitively proven.
  • Predictions: A specific, testable outcome derived from a hypothesis; typically formulated as an "if/then" statement.

Methodologies in Science

Descriptive Science (Relies on Inductive Logic)

  • Inductive Logic: Involves making broad, general statements or conclusions from many specific observations.
  • Process: Descriptive science formulates general conclusions about the natural world based on a large number of observations.
  • Impact: Some of society's most significant intellectual advances (e.g., those by Isaac Newton and Charles Darwin) resulted primarily from descriptive science.
  • Outcome: The result can be the formation of a scientific "law" or a testable hypothesis.

Experimental Science (Relies on Deductive Logic)

  • Deductive Logic: Involves making specific predictions from a general statement or hypothesis.
  • Process: Experimental science tests these specific predictions that are derived from hypotheses.
  • Outcome: The results yield more data that either specifically supports or refutes a hypothesis.
  • Example: Peter and Rosemary Grant's long-term study of Darwin's Finches on the Galápagos Islands, where they tested specific predictions derived from the broader theory of natural selection.

Everyday Problem Example (Lamp)

  • Scenario: A lamp isn't working.
  • Scientific Questions to Ponder:
    • If an experiment is conducted (e.g., checking the bulb), did the experimenter prove that the bulb was burned out?
    • Did the experimenter prove that a burned-out bulb was the sole reason the lamp wasn't working (as opposed to a faulty wire, switch, etc.)?
    • This example underscores the concept that experiments can support or refute, but rarely prove an explanation conclusively.

General Rules for Scientific Inquiry

Rule #1: Facts/Data as Basis of Arguments

  • Principle: Facts and data are considered true and must form the bedrock of any scientific argument, opinion, or conclusion.
  • Quote: Thomas Huxley's poignant observation: "The great tragedy of science is the slaying of a beautiful hypothesis by an ugly fact."

Rule #2: Hypotheses and Theories Cannot Be Proven True (But Can Be Falsified/Supported)

  • Principle: While hypotheses and even well-established theories can be strongly supported by evidence or demonstrated to be false (falsified), they can never be definitively proven absolutely true.
    • This is a core tenet of the philosophy of science, recognizing the provisional nature of scientific knowledge.

Detailed Example: Cryptic Coloration in Mice (Vignieri et al., 2010)

Observation and Inductive Logic
  • Observation (Fact): The fur color of a mouse tends to match its surroundings.
  • Inductive Reasoning: Repeated observations of this phenomenon lead to a general rule or conclusion: "Most animals blend into their habitat."
    • This process exemplifies descriptive science and inductive logic.
Forming a Hypothesis
  • Definition: A hypothesis is a possible explanation for an observed phenomenon (a set of facts).
  • Structure: It is typically framed as a "Phenomenon-because" statement (e.g., "Phenomenon XX happens because of YY").
  • Example for Mice: The fact that mouse fur color matches the environment can be explained because well-camouflaged mice are less likely to be preyed upon by predators (i.e., cryptic coloration is adaptive and provides a survival advantage).
Falsifiability and Predictions
  • Principle: A robust scientific hypothesis must be falsifiable (refutable) through experimentation.
  • Experiment: An experiment is designed to test a specific prediction derived from the hypothesis.
  • Prediction Structure: Predictions are explicit "If-then" statements.
  • Example Prediction for Mice:
    • IF fur color matches habitat because well-camouflaged mice are less likely to be preyed upon,
    • THEN mice that do not match their habitat should be preyed upon more frequently than mice that do match their habitat.
  • Outcome: An experiment can either confirm or falsify a prediction.
    • If the prediction is falsified, the hypothesis is deemed incorrect or requires significant revision.
    • If the prediction is upheld (supported), it strengthens the argument for the hypothesis, but it does not constitute absolute proof.
Experimental Results and Controls (Vignieri et al., 2010)
  • Experimental Design: Researchers used model mice and counted predation attempts.
  • Results: Mice models that did not match their habitat were indeed preyed upon more heavily.
  • Interpretation (following Rule #1): These facts strongly support the hypothesis that mice have fur that matches their surroundings because it helps them avoid being eaten. However, it is still acknowledged that this is not proven absolutely.
  • Importance of Controls: Well-designed experiments include controls to rule out alternative explanations.
    • Control in Mouse Experiment: The experiment was conducted in both environments (e.g., dark mice in dark habitat and light habitat, and vice versa for light mice). This control served to rule out an alternative hypothesis: that predators might simply prefer one variant of model mouse over another, regardless of camouflage. Without conducting the experiment in both environments, this alternative explanation could not have been eliminated. The comparison of predation rates between matching and non-matching models in both habitats is a critical control.

Detailed Example: Evolution of Life Classification

  • Historical View (~30 years ago): Life's diversity was primarily described using a system of 5 kingdoms.
    • These included "Prokaryotes" and "Eukaryotes" (further divided into Protists, Fungi, Plants, Animals).
  • Carl Woese's Hypothesis: Woese proposed a radically different organizational structure for life's diversity, suggesting three primary domains.
    • This hypothesis gained substantial support and became the widely accepted Theory of Three Domains: Domain Bacteria, Domain Archaea, and Domain Eukarya.
    • This illustrates how hypotheses, with sufficient evidence, can become accepted theories, replacing older models.
  • Recent Theoretical Developments: Even established theories can be refined or challenged by new evidence.
    • A more recent theory (which may not yet be in standard textbooks) challenges the equivalence of the three domains.
    • It posits that Bacteria and Archaea are Primary Domains, and Eukarya is a Secondary Domain.
    • This theory suggests that Eukarya originated approximately 22 billion years ago through an event of endosymbiosis, where a Bacteria was incorporated by an Archaea.
    • This continuous evolution of understanding exemplifies Rule #2: theories are never truly proven but are dynamic, subject to revision and replacement as new evidence emerges.

Understanding Scientific Theories

  • Distinction from Everyday Language: It is crucial not to confuse the scientific meaning of "theory" with its colloquial use, where it might imply a speculative idea or an educated guess.
  • Scientific Definition: A scientific theory is a broad, overarching explanation or hypothesis that is extraordinarily well-supported by a massive body of observations and rigorous experiments.
  • Characteristics and Utility:
    • Theories are robust explanations widely considered to be correct.
    • They explain a vast array of observations and phenomena.
    • They are immensely useful because they provide a comprehensive framework for understanding the world.
    • Confirmation of Predictions: A key characteristic of a correct theory is that many predictions derived from it should be consistently confirmed through further research and experimentation.
  • Example: The Theory of Natural Selection: This theory explains why organisms are so well adapted to their particular environments (e.g., the mouse coat color example). It is also one of the fundamental mechanisms explaining how organisms evolve over time.

Example: Cellular Theory of Life

  • Core Tenets:
    1. All known living things are fundamentally made from cells. (This implies that entities like viruses, which are not cellular, are often not considered truly "living" within this framework).
    2. All existing cells originate exclusively from pre-existing cells (i.e., they do not arise spontaneously from non-cellular matter).
  • Predictions from Cellular Theory:
    1. If a new living organism is discovered, it is predicted that it will invariably be composed of cells.
    2. Observations of new cell formation will only ever show them being created through some form of cell division (e.g., mitosis, meiosis, binary fission).
  • The consistent confirmation of such predictions reinforces the strength and utility of the Cellular Theory.