KP

Scientific Method Vocabulary Flashcards

What is Science?

  • Science etymology: derived from Latin roots meaning “to know.”
  • Notable examples and contributions:
    • Louis Pasteur: Pasteurization; Germ Theory of Disease; Vaccines.
    • Charles Darwin: Theory of Evolution by Natural Selection.
    • Victor Ambros & Gary Ruvkun: Discovery of microRNA and its role in post-transcriptional gene regulation.
  • Core idea: science is a systematic approach to know natural phenomena through evidence and reasoning.

How do we “know” things? Inquiry

  • INQUIRY: the search for information and explanations of natural phenomena.
  • Reliance on observations and published observations (DATA).
  • Examples mentioned:
    • Thermal adaptation & plasticity of the plant circadian clock.
    • Developmental stages in plants.
  • Scientists use a process that includes making observations, forming logical hypotheses, and testing them (Chung-Mo Park).
  • Case example:Thermal adaptation and the discovery that roots can be involved in photosynthesis overturning long-accepted notions.

Who developed the scientific method?

  • No single inventor; refined over time by multiple thinkers:
    • Aristotle (384–322 BC): early groundwork.
    • Francis Bacon: advocated empirical observation and inductive reasoning.
    • René Descartes: emphasized deductive reasoning.
    • Galileo Galilei: quantitative measurements; challenged era’s paradigms with the heliocentric model.

Inductive vs. Deductive Reasoning (Key Reasoning Types)

  • Inductive reasoning: derives generalizations from many specific observations.
    • Francis Bacon example: Sun rises in the East on Monday, Tuesday, Wednesday → generalization: the sun always rises in the East.
    • Characteristic: IR (inductive reasoning) because it builds general conclusions from specific data.
    • Visual: Observation → Observation → Observation → Generalization.
  • Deductive reasoning: uses general premises to make specific predictions.
    • René Descartes example: Cogito, ergo sum — I think, therefore I am.
    • Structure (illustrated):
    • Premise 1 (General Truth): Anything that thinks must exist.
    • Premise 2 (Observation): I am thinking.
    • Conclusion: Therefore, I exist.
    • Universal principle (thinking ⇒ existence) → Specific case (I) → I exist.

The Process of the Scientific Method (Overview)

  • Observation: e.g., some people sneeze when exposed to pollen.
  • Question: Why do people sneeze when exposed to pollen?
  • Hypothesis: If a person is exposed to pollen, they will sneeze because their immune system overreacts to the pollen as a harmful substance.
  • Test: observe an individual’s reaction to pollen inhaled and placed under skin.
  • Research: search and understand published data on pollen allergenicity.
  • Analyze & Conclude: Is sneezing correlated with a positive skin-prick test?
    • HO (null hypothesis): no significant difference (no correlation).
    • HA (alternative hypothesis): significant difference (correlation).
  • Publish (communicate results): share findings with the field.

Steps of the Scientific Method (Detailed List)

1) Observe
2) Ask a question
3) Do research
4) Construct hypothesis
5) Test hypothesis – experiment
6) Analyze data & draw conclusion
7) Communicate results

Example: Process of the pollen-sneeze hypothesis (Expanded)

  • Observation: Some people sneeze when exposed to pollen.
  • Question: Why do people sneeze when exposed to pollen?
  • Hypothesis: If a person is exposed to pollen, they will sneeze because their immune system overreacts to the pollen as a harmful substance.
  • Test: observe an individual’s reaction to pollen inhaled and placed under skin.
  • Research: search & understand published data on pollen allergenicity.
  • Analyze & Conclude: Is sneezing correlated with a positive skin-prick test?
    • HO: no significant difference (no correlation)
    • HA: significant difference (correlation)
  • Publish (communicate results): tell others in the field what you observed.
  • Null vs Alternative Hypotheses: formal distinction used to test expectations.

Hypothesis, Theory, & Law: Definitions

  • Hypothesis: a tentative and testable explanation based on observations.
    • Example: If plants get more sunlight, they will have increased biomass.
  • Theory: a well-substantiated explanation of a natural phenomenon, supported by extensive evidence, repeated testing, and multiple lines of data.
    • Example: Theory of Evolution by Natural Selection.
  • Law: a concise statement or mathematical equation describing a consistent, observable pattern in nature, often without explaining why it occurs.
    • Example: Law of Conservation of Energy; Law of Gravity.

Characteristics of Hypothesis, Theory, & Laws

  • Hypothesis
    • Specific and falsifiable (can be proven wrong).
    • Formed early in the scientific method.
    • Often phrased as an “if-then” statement.
  • Theory
    • Broad, comprehensive, and based on many experiments and observations.
    • Can be modified as new evidence emerges but is highly reliable.
    • Explains why something happens.
  • Law
    • Universal and highly reliable, based on repeated observations with no known exceptions.
    • Often expressed mathematically.

Differences among Hypothesis, Theory, & Law (Key Aspects)

  • Scope:
    • Hypothesis: Narrow, specific prediction.
    • Theory: Broad explanation of phenomena.
    • Law: Describes a specific, consistent pattern.
  • Evidence Level:
    • Hypothesis: Preliminary, untested or lightly tested.
    • Theory: Extensively tested and widely accepted.
    • Law: Universally observed; no known exceptions.
  • Purpose:
    • Hypothesis: Proposes a testable idea.
    • Theory: Explains why phenomena occur.
    • Law: Describes what happens under specific conditions.
  • Certainty:
    • Hypothesis: Tentative, subject to revision.
    • Theory: Highly reliable but open to refinement.
    • Law: Considered universally true (within its domain).
  • Examples:
    • Hypothesis: "Pollen causes sneezing in some people."
    • Theory: Germ Theory of Disease.
    • Law: Law of Gravity.

The Place of the Scientific Method in Knowledge

  • Experimental Data: Core but not the Whole. Rooted in empiricism (knowledge from observation) and skepticism (questioning assumptions). Assumes natural laws are consistent.
  • Constraints: Some phenomena (e.g., black holes, historical events) can’t be experimentally tested; rely on observations or models.
  • Observational Studies & Real-World Evidence: e.g., cohort/case-control studies; provide insights where experiments are impractical or unethical (smoking and lung cancer; cortisol and weight gain; aluminum salts in vaccines and disorders – notes examples used in slides).
  • Theoretical Models & Simulations: Predict outcomes and guide experiments (e.g., modeling neurotransmitter dynamics; hypothesizing mechanisms before experimental validation).
  • Peer Review & Scientific Consensus: Scrutinizes data, methods, and conclusions to ensure reliability.
  • Interdisciplinary Synthesis: Combines insights from multiple fields to enrich understanding (e.g., Methylene Blue’s biochemical effects, neuroscience (fMRI), and psychology (memory performance)).

Paradigm Shifts and New Knowledge

  • Paradigm Shift: A fundamental change in how we understand the world; occurs in industry, science, and personal perspectives.
  • Waggle Dance (Karl von Frisch, Nobel Prize 1973): An example of contested new knowledge.
    • Controversy: Some scientists questioned the interpretation of the waggle dance; opponents argued evidence could be explained by olfactory cues rather than language.
    • Wells et al.: Controlled experiments showing data can be explained by olfactory cues.
    • Analyses (Grok): Olfactory vs language interpretations; ongoing debates illustrate how new evidence can shift or refine paradigms.
  • Important ethical dimension: Dark Science refers to potential dangers and ethical dilemmas associated with new knowledge.

New Knowledge and Its Impact on Science

  • New findings may lead to paradigm shifts, requiring reevaluation of established ideas.
  • Importance of testability and falsifiability in preventing dogmatic claims and pseudoscience.

Visualizing Variables in Experiments

  • Independent Variable (X-axis): The factor deliberately changed or controlled (e.g., Time, Temperature).
  • Dependent Variable (Y-axis): The outcome measured in the experiment (e.g., Speed, Growth, Response).
  • Diagramming tip: Place the independent variable on the X-axis and the dependent variable on the Y-axis to interpret causal relationships.

Example: The Cat Hypothesis (Why the Cat Knocks Things Off)

  • Observations and questions can lead to problematic hypotheses when they attribute mental states or intentions to animals (e.g., training humans, asserting dominance).
  • This is scientifically problematic because we lack direct evidence of beliefs or intentions in animals, making such hypotheses difficult or impossible to test meaningfully.
  • Takeaway: Ensure hypotheses are testable with measurable variables and avoid attributing untestable mental states.

Testability vs Falsifiability (Important Distinctions)

  • Testability: A hypothesis must be investigable with measurable variables and a clear method to collect evidence; experiments should be repeatable.
  • Falsifiability: A hypothesis must be open to being disproven; allows results that contradict the claim; helps avoid vague or untestable assertions and pseudoscience.
  • Cat example and pollen sneeze example illustrate these concepts in practice.

Null vs Alternative Hypothesis (Formalizing Testing)

  • Null Hypothesis (H0): There is no significant difference or effect.
  • Alternative Hypothesis (HA): There is a significant difference or effect.
  • In pollen example:
    • H_0: ext{There is no significant correlation between pollen exposure and sneezing (no effect).}
    • H_A: ext{There is a significant correlation between pollen exposure and sneezing (effect).}
  • In physics examples:
    • Law of Gravity: F = G \frac{m1 m2}{r^2}
    • Conservation of Energy: E{ ext{total}} = Ek + E_p = ext{constant}.

Mathematical and Logical Representations in the Method

  • If-then structure of hypotheses: ext{If } X ext{ changes, then } Y ext{ changes accordingly.}
  • Graphical representation: Independent variable on the X-axis, dependent variable on the Y-axis (as in the variable diagram).
  • Formal hypothesis notation: H0: ext{no effect} \ HA: ext{effect exists}

Connections to Foundational Principles and Real-World Relevance

  • Foundational principles: empiricism (knowledge from observation) and skepticism (questioning assumptions).
  • Real-world relevance: understanding how scientists build knowledge helps assess public claims, science communication, and policy decisions.
  • Ethical and philosophical implications: responsible interpretation of data, avoidance of pseudoscience, and consideration of potential societal impacts (as highlighted by discussions of Dark Science and paradigm shifts).

Learning Objectives (Recap)

  • Explain how deductive and inductive reasoning drive the process of science.
  • Draw the process of science: indicate the primary steps and label their order with arrows.
  • Compare hypothesis vs theory vs law using arrows; assess good vs bad hypotheses in terms of testability/falsifiability and the roles of dependent and independent variables.

Practical Notes for Exam Preparation

  • Be able to identify an inductive vs a deductive argument from examples.
  • Be able to outline the seven steps of the process of science and describe what happens if steps are reordered.
  • Be able to differentiate a hypothesis, a theory, and a law with at least one example each.
  • Be able to describe the roles of dependent and independent variables and how they are represented on a graph.
  • Understand the concepts of testability and falsifiability and apply them to a given claim.
  • Recognize that scientific knowledge sits within a continuum of data, models, observations, and peer-reviewed consensus, not as a single absolute truth.

Note: Throughout these notes, I’ve included explicit references to the examples and phrasing used in the provided transcript (e.g., pollen sneeze example, pollen allergenicity research, waggle dance debates, and Campbell’s biology references) to ensure coverage of both major and minor points discussed.