Scientific Inquiry and Foundations for BIO 201 — Lecture Notes

Overview: science as an iterative process of inquiry

• Science is a process of inquiry: observe, question, and try to understand the natural world through repeated cycles.
• Inquiry can operate with or without a predefined hypothesis. In some natural sciences (e.g., oceanographic cruises, long-term monitoring stations), researchers collect large amounts of data first and look for patterns before forming hypotheses.
• The overarching structure is iterative, not a strictly linear path:
  • Observation → Question → Hypothesis/Prediction → Test → Evaluate data → Decide if the hypothesis is supported or rejected, not proven.
  • Real science often loops back: new observations may lead to new hypotheses, or additional factors must be controlled or considered.
• Practical implication: this framework helps frame foundational biology (BIO 201) and builds toward the ability to generate testable hypotheses from observations.

The iterative nature of the Scientific Method

• General structure (not strictly linear):
  • Observation
  • Question
  • Hypothesis or tentative educated guess
  • Prediction (if the hypothesis is true, then this should happen)
  • Test/Experiment
  • Evaluate data
  • Support or reject the hypothesis (never absolutely prove) → may lead to refinement or new hypotheses.
• When testing, control for one variable at a time to avoid confounding factors.
• Even simple demonstrations (e.g., a desk lamp) illustrate multiple explanations and the need for controlled testing.

Example: Desk lamp scenario illustrating hypothesis testing

• Observation: the desk lamp does not turn on.
• Question: why is the lamp not working?
• Possible hypotheses:
  • H1: the bulb is burned out.
  • H2: the bulb is not screwed in properly.
• Prediction for each hypothesis:
  • If H1 is true, then replacing the bulb should fix the lamp.
  • If H2 is true, then screwing the bulb in should fix the lamp.
• Test: replace the bulb and observe whether the lamp works.
• Interpretation:
  • Replacing the bulb may make the lamp work, but this does not prove which specific fault existed; there could be other factors (e.g., power surge, wiring issue).
  • The conclusion is: the hypothesis is supported by the data, not definitively proven.
• Important nuance:
  • If the power to the building surges or drops, the lamp can appear to be fixed by bulb replacement even if the underlying issue wasn’t the bulb.
  • This illustrates that a single data point or test cannot establish absolute truth; more data and controlled testing are needed.

Key concepts: hypotheses, predictions, and control

• Hypothesis: a tentative, educated guess that can be tested via experimentation.
• Prediction: a logical consequence of a hypothesis (a statement that can be tested).
• Control group: a baseline or standard against which results are compared.
• Control of variables: test one variable at a time to isolate its effect.
• When experiments generate substantial evidence across many observations, a hypothesis may contribute to building a theory.

Deductive vs Inductive reasoning in science

• Deductive reasoning (general → specific):
  • Start with a general principle and derive specific conclusions.
  • Example: If the sun always rises in the East (general principle) and you observe the sun in the East, you infer it is morning (specific conclusion).
  • Formalized idea: G
    ightarrow S where G is a general principle and S is a specific outcome.
• Inductive reasoning (specific → general):
  • Start with many specific observations and generalize to a broader principle.
  • Example: Observing the sun rising in the East on many days leads to the general statement that the sun rises in the East.
  • Formalized idea: from multiple observations S1, S2,
    ots
    , S_n
    ightarrow G where G is a general principle.
• The point: scientists use both directions of reasoning to make sense of data and to generate/test theories.

The role of facts, hypotheses, and theories in learning biology

• The course emphasizes not just memorizing facts but developing a framework to form hypotheses when presented with observations.
• By the end of the semester, students should be able to form plausible hypotheses about novel cell observations based on foundational knowledge.
• Hypotheses vs Theory:
  • Hypothesis: a testable statement that guides experiments.
  • Theory: a well-supported, broad explanation backed by a large body of evidence from many experiments and researchers.
  • Examples of theories (conceptual sense): the theory of relativity, the theory of evolution.
• A hypothesis may be supported by many observations; as evidence accumulates, it may contribute to a theory.
• Important distinction: a theory is not a guess; it is a robust, extensively tested framework.

Why does this matter for scientific thinking in BIO 201?

• The purpose of learning these processes is to enable flexible thinking when examining cells and biological phenomena.
• When you see an unusual cell, a solid foundation allows you to generate a plausible hypothesis (e.g., about the cytoskeleton or cell division) and design experiments to gather more information.
• Case study practice helps you apply general principles to specific observations, bridging theory and experiment.
• You will learn to avoid overclaiming from a single observation, instead building a coherent set of evidence to support or reject a hypothesis.

The terminology and progression from hypothesis to theory in practice

• Hypothesis: tentative explanation tested by experiments and observations.
• When many independent lines of evidence converge, a hypothesis can become a theory.
• Theories are not proven in an absolute sense; they are supported by cumulative data.
• Theories may be refined or extended as new data emerge.

Limitations of science: scope, ethics, and practicality

• Science has limits and cannot answer every kind of question:
  • Not all questions are empirical or testable; some relate to values, beliefs, or morality.
  • Some questions are philosophical or theological and extend beyond the scope of testable hypotheses.
  • Some questions may be unanswerable with current tools or knowledge.
• Potential biases and limitations:
  • Scientists bring biases and blind spots; replication and collaboration across diverse groups help mitigate this.
  • Bias can affect interpretation and reporting; multiple studies and independent verification improve reliability.
• Practical and ethical constraints:
  • Some experiments may raise ethical concerns or require prohibitive resources; not all hypotheticals can be tested.
  • Science does not provide direct answers about how to use technology; ethical and societal considerations require human input and discourse.
• Precision and measurement limits:
  • Some questions require extremely precise measurements; advances in technology improve capabilities but do not guarantee absolute certainty.
• Mechanistic explanations vs. ultimate why:
  • Science often addresses mechanism (how things work) rather than ultimate purpose or why they exist.
  • Some questions may lie outside empirical testing (e.g., supernatural explanations or belief-based questions).
• The social nature of science:
  • Science is a collective enterprise, best advanced by diverse researchers and institutions to approach a closer approximation of reality.
  • Reproducibility, peer review, and transparent methods are essential for building credible knowledge.

Connecting to real-world science and classroom practice

• This framework helps students engage with real-world problems by forming testable hypotheses from observations and designing controlled experiments.
• Case studies and exercises in BIO 201 will reinforce the practice of hypothesis generation, controlled testing, and interpretation of results.
• The ultimate goal is to empower students to think critically, reason rigorously, and communicate findings effectively, even when data are imperfect or ambiguous.

Preview: next week

• The course will delve into the biology of cells, applying these scientific reasoning skills to understanding cell structure and function.
• Expect to practice identifying hypotheses from observations, designing experiments, and evaluating evidence in the context of cellular biology.