Comprehensive Study Notes on Inductive/Deductive Reasoning and Scientific Method
Inductive vs Deductive Reasoning
Inductive reasoning: starts very specific and moves toward a broader viewpoint.
You go out and make specific observations (e.g., in the backyard, observe that flowers grew this month, repeat observations).
From these specific observations you draw a very general conclusion and can make predictions.
Example from early animal behavior: observe that there are always geese at a pond in winter and never at the same location in summer; infer a general pattern of migration and predict geese will appear again next winter but not next summer.
Deductive reasoning: starts from a broad principle and becomes more specific.
You begin with a general truth, theory, or law, and you use it to predict specific results or test a hypothesis.
Natural selection example (as discussed): if individuals with certain adaptations survive and reproduce more, then populations over generations will show those adapted traits more frequently. Apply this to a pond scenario with fish of different colors to predict which color traits persist based on predation and camouflage.
Descriptive vs hypothesis-based science tied to reasoning styles
Descriptive science (often descriptive in nature): mainly observational, aims to describe and observe the world.
Hypothesis-based science: starts with a question and a testable hypothesis, then tests it with experiments.
Examples contrasting inductive/descriptive vs deductive/hypothesis-based approaches
Descriptive example: researchers watching a population of field mice, noting behaviors at different times of day; they observe and record without asking a question or formal prediction, leading to inductive conclusions.
Hypothesis-based example: researchers predict activity levels change due to temperature and conduct experiments to test that specific hypothesis.
The scientific method: a flexible, non-linear process
Historically taught as a rigid sequence, but in practice it’s iterative and fluid: observations, questions, hypotheses, experiments, and revisions often loop back.
Emphasis is on logical thinking and the ability to reason through problems, not slavishly following a fixed set of steps.
Hypothesis formulation: two key requirements
Testable (quantifiable): there must be a way to measure and collect data to test the hypothesis.
Falsifiable: there must be some possible outcome that could disprove the hypothesis; otherwise it cannot be scientifically tested.
Example of testable hypothesis: "Students who study for more hours outside of class will have higher grades." Measure study hours and correlate with grades.
Example of non-testable hypothesis: "Studying outside of class makes you a better person." Morality is not directly measurable, so this is not testable.
The problem of falsifiability and the idea of being potentially wrong
In science you can never be definitively “right” in the long run because future data could falsify your hypothesis.
A falsifiable hypothesis allows for possible refutation, which strengthens conclusions when multiple data sets support it.
Classic illustration: swans. If you have only seen white swans, your hypothesis would be "all swans are white"; finding a black swan falsifies it.
Experimental testing: variables and groups
Independent variable: the factor that the researcher actively changes/manipulates (e.g., fertilizer application).
Dependent variable: the outcome measured (e.g., number of flowers).
Experimental group: receives the treatment (e.g., fertilizer).
Control group: does not receive the treatment, but is otherwise kept under the same conditions to provide a baseline.
The general aim is to observe whether changes in the independent variable cause changes in the dependent variable, while holding other factors constant.
A practical example: fertilizer and orchids
Hypothesis: Miracle Gro fertilizer will increase the number of orchid flowers.
Independent variable: fertilizer application (yes vs no).
Dependent variable: number of flowers produced.
Experimental group: orchids with fertilizer; Control group: orchids without fertilizer.
Important note: ensure constant conditions (sunlight, watering, temperature) to isolate the effect of the fertilizer.
Real-world example: Alexander Fleming and penicillin
Fleming left petri dishes uncovered and observed mold (Penicillium) growing near bacteria.
He noticed that bacteria did not grow on top of the mold, suggesting the mold secreted a substance that inhibited bacteria.
This led to isolation of penicillin, the first true antibiotic, derived from the mold.
Another applied example: yogurt and dog bone density
Example setup: feed yogurt to one group of dogs while another group receives standard dog food; measure bone density after a period.
Independent variable: yogurt supplementation.
Dependent variable: bone density.
Experimental group: dogs receiving yogurt; Control group: dogs not receiving yogurt.
Basic (pure) science vs applied science
Basic science (pure science): often descriptive, exploratory, and foundational; aims to expand knowledge without immediate practical applications; historically associated with older papers and descriptive observations (e.g., birds) and inductive reasoning.
Applied science: aims to solve concrete problems and develop new technologies or therapies; often hypothesis-based and problem-oriented; vaccine development and cybersecurity are examples.
Communication of findings: disseminating results
Conferences, poster presentations, and meetings are traditional avenues but reach narrower audiences.
Peer-reviewed journals provide broader access via open or restricted access formats.
The MRAD format (Introduction, Methods, Results, and Discussion) is a core structure in most peer-reviewed papers used in lab reports and publications.
Peer review process:
Submitting a manuscript triggers evaluation by typically four reviewers who assess originality, significance, logical reasoning, and the thoroughness of the methodology.
Reviewers’ feedback may require revisions; after resubmission, the paper may be re-evaluated until acceptance.
The process is designed to ensure that published findings are credible and reproducible, beyond mere grammatical correctness.
Publication models and access
Historically, journals existed as expensive hard copies; subscriptions could be extremely costly (examples mention extremely high subscription costs).
Online and open-access formats have greatly increased accessibility; many journals are free or low-cost, though some require subscriptions.
University libraries often provide access to journals; researchers can request articles through their libraries if direct access is not available.
Ethics and bioethics in science
Much foundational science was conducted before modern ethical review boards; today’s ethics frameworks aim to protect people and organisms from harm.
Notable unethical studies historically cited:
The Tuskegee Syphilis Study (1930s): enrolled Black men with syphilis and withheld treatment to study disease progression.
Spinal tap experiments on children: conducted without proper parental consent in some cases.
Hepatitis transmission studies in prisons: infected prisoners to study disease spread, with broader public health implications.
These cases highlight the tension between scientific knowledge gains and ethical considerations.
Henrietta Lacks and HeLa cells: HeLa cell line legacy raises important ethical questions about consent, ownership, and benefit-sharing in biomedical research.
Socio-scientific issues: many scientific findings have social implications and ethical dimensions; modern research often intersects with policy, equity, and morality.
Framing the course discussion on ethics
A directed reading assignment: read the last section of Chapter 1, Section 1 about scientific ethics (the paragraph on Henrietta Lacks and HeLa cells).
In-class Friday activity: students will respond to prompts about scientific ethics and discuss perspectives.
In-class activity prompt (practice exercise)
Prompt: Is one type of reasoning or approach (inductive/descriptive vs deductive/hypothesis-based) better or more important? Why or why not?
Students can respond with bullet points or short sentences; the instructor will collect and discuss responses to illustrate differing viewpoints and the value of multiple approaches.
Quick recap of terminology to remember
Inductive reasoning: specific observations → general conclusions
Deductive reasoning: general principle → specific predictions/hypotheses
Descriptive science: observational, inductive, descriptive of the world
Hypothesis-based science: testable predictions tested via experiments (deductive)
Independent variable: the factor you manipulate
Dependent variable: the outcome you measure
Experimental group: receives the treatment
Control group: baseline for comparison
MRAD: Introduction, Methods, Results, and Discussion (structure of most peer-reviewed papers)
4 sections: the standard MRAD framework comprises the core sections of a paper
Open access vs paywalled: broader vs restricted access to published work
Bioethics: ethical considerations in research involving people, animals, and broader societal impacts