Chapter 1 Part 3: Scientific Method: Hypotheses, Theories, Laws & Experimental Design

Steps previously covered:
- Observation → ask a question.
- Form a good hypothesis: must be clear, testable, falsifiable.

"Crystal power heals the soul"
- ✗ Not testable ("soul" cannot be operationally defined or measured).
- ✗ Not falsifiable.
"Country music is the best type of music"
- ✗ Subjective; relies on personal taste.
- ✗ "Best" lacks measurable criterion.
"Hunting species to extinction is wrong"
- ✗ Moral/ethical statement, not experimentally testable.
"Changing the amount of fertilizer given to crops will affect their growth"
- ✓ Clear variables: fertilizer quantity & plant growth.
- ✓ Testable by varying fertilizer levels.
- ✓ Falsifiable: possible to obtain data showing no growth difference.

Hypothesis
- Addresses a single, specific experiment or event.
- Initial, tentative explanation.
Theory
- Began as a hypothesis but supported hundreds/thousands of times under diverse conditions by multiple researchers.
- Operating framework until reproducibly disproven.
- Biology staples: Cell Theory & Theory of Evolution.
Principle/Law
- Statement universally observed with virtually no exceptions (e.g., law of gravity).
- Rare in biology; Mendel’s Laws sometimes cited as exceptions.
Language note: In everyday speech people say “I have a theory,” but technically they have a hypothesis; popular media often mis-uses the term.

Must directly test the hypothesis; a weak hypothesis → weak experimental design.
Controls
- Provide baseline for comparison.
- Types: positive, negative, or simply “the control.”
- Fertilizer example: each plot receives equal water; control plot lacks fertilizer so only fertilizer varies.
Data Collection Modes
- Qualitative (descriptive): color, texture, foamy, etc. Good if the research question is descriptive.
- Quantitative (numeric): measurable values preferred for statistical analysis.

Scientists love statistics for objectivity.
Common tests: ANOVA, t-test, \chi^2, regression, correlation.
"Significant difference" typically means probability of observing the result by chance is < 5\% ( p < 0.05 ).
Graphs enhance pattern recognition:
- X-axis (independent variable) – e.g., time, fertilizer type.
- Y-axis (dependent variable) – e.g., plant height.
- Clusters reveal similarity; gaps reveal difference.

Decide whether to support or reject the hypothesis.
Rejecting isn’t failure—can refine hypothesis or identify confounding variables.

Preferred avenue: peer-reviewed scientific journals.
- Manuscript evaluated by subject-matter experts for methodology & interpretation.
- Not all studies are published (e.g., instructor’s soil-sound study rejected for using \$200 microphones instead of \$2000 geophones).
Primary vs. Secondary Sources:
- Primary = original peer-reviewed article.
- Secondary = summaries (newspapers, magazines).
- Tertiary/opinion pieces may contain bias or errors.

Always consider the source and potential agenda:
- Opinion editorials masquerading as news.
- Paid spokespeople or actors ("stethoscope effect").
- Check authors’ credentials & funding.
Strategy: whenever possible, trace statements back to the primary data.

Presents beliefs as "science" without reproducible data.
Tactics & clues:
- Grand claims with no empirical evidence.
- Emotional appeals, hand-waving, hostility to critical questions.
- Reliance on anecdotes ("friend of a friend").
- Claims "intuition" or secret knowledge.
Common examples: astrology, magic, UFOs, cryptids (Bigfoot, Loch Ness Monster).
- Class anecdote: biomass calculations for Loch Ness left 2000\,\text{kg} unaccounted → humorous “proof” of Nessie.

Biology = study of life; scientific method underpins every topic we’ll cover.
Upcoming chapters will expand on foundations (cell theory, genetics, evolution, etc.).
Recommended: read textbook chapter, review these notes, and watch lecture videos for reinforcement.