BIOS 110 Comprehensive Study Notes (Notes from Transcript)
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Science as a Body of Knowledge and Theories
A scientific theory is a well-supported, evidence-based explanation of the natural world.
It is built from a collection of insights and observations, rigorously tested and refined over time.
Theories are not hunches or casual guesses—they are robust frameworks that help us understand and predict phenomena.
You cannot simply reject a theory because it conflicts with personal beliefs or preconceived notions.
Theories are powerful tools for understanding nature; they guide future research, inspire new questions, and help us explore unknown aspects of nature.
Theories have predictive power.
Examples of theories:
The Big Bang Theory
The Theory of Gravity
The Theory of Electricity
The Theory of Evolution
Theories: robust frameworks with predictive power, not arbitrary guesses.
Scientific Use of "Theory" and Common Misconceptions
Science uses the term THEORY to denote explanation supported by evidence, not a casual guess.
It’s not "just a theory" in the sense of weak truth; it is fortified by facts.
Examples and notes:
Endosymbiotic Theory
Cell Theory
Scientific theories do not graduate into laws.
Public/casual use of "theory" may reflect opinion, hunch, or speculation.
The difference between theory (scientific understanding) and everyday speculation is crucial for scientific literacy.
Scientific Language vs. Everyday Language (Key Terms Mapping)
Scientific Term → Public Meaning
enhance → improve, intensify, increase
aerosol → spray can → tiny atmospheric particle
positive trend → good trend → upward trend
positive feedback → good response, praise → vicious cycle, self-reinforcing cycle
theory → hunch, speculation → scientific understanding
uncertainty → ignorance → range (context-dependent)
error → mistake, wrong, incorrect → difference from exact true number
bias → distortion, political motive → offset from an observation
sign → indication, astrological sign
plus or minus sign → values
ethics, monetary value → numbers, quantity
manipulation → illicit tampering → scientific data
processing scheme → devious plot → systematic plan
anomaly → abnormal occurrence → change from long-term average
Science is Always Open to Revision and Independent Confirmation of Results
Every assertion regarding the natural world is subject to challenge and revision based upon old and new evidence.
Scientists encourage independent confirmation of their results.
This does NOT mean you are free to pick and choose what you "believe" to be true.
Observation and Hypothesis: Brazil and Western Africa Case
Observation: Many plants, animals, and geologic formations in Brazil are similar to those in Western Africa.
Hypotheses to explain the similarity:
A. There was a large continent connecting these two areas (which has since sunk).
B. There were narrow land bridges connecting these two areas (which have since sunk).
C. Similar environmental conditions led to convergent evolution, resulting in similar species and formations.
D. Species and seeds were dispersed across the Atlantic via ocean currents or floating vegetation.
E. The similarities are due to misinterpretation of fossil or geological data.
Additional note (page 8-10): Hypothesis F – Brazil and Western Africa were once part of a single supercontinent (e.g., Gondwana) that split due to plate tectonics. Hypotheses labeled as disproven appear in some slides.
Scientific Models
What is a model? A model is a simplified reconstruction of nature, created to help us study and understand complex phenomena.
Why use models? Nature can be both simple and complex; models can explain phenomena using a few key principles.
Key features of models:
Simplification: leaves out complicating details to focus on core factors.
Abstraction: uses one factor to represent a group of related influences.
Unrealistic assumptions: may include idealized conditions (e.g., no friction, random mating) to simplify analysis.
Important insight: A model that includes everything would be as complex as the real world (and hard to understand).
A model does not need to be perfectly accurate to be useful.
Examples:
The Bohr model of the atom
Light as a wave or particle
Experimental Design Principles
Purpose: Experiments are designed to test the validity of a hypothesis.
Falsifiability: A strong experiment aims to disprove the hypothesis; hypotheses that withstand testing are robust.
Simplicity & Repeatability:
Simple: minimize steps and complexity.
Repeatable: others should be able to replicate results.
Controlled: limit the number of variables to isolate effects.
Randomization: Random assignment helps reduce bias and confounding factors.
Variables in Experimental Design
Independent Variable (Experimental Variable)
Definition: The variable deliberately changed or manipulated by the experimenter.
Purpose: To observe its effect on the dependent variable.
Dependent Variable (Outcome Variable)
Definition: The variable measured or observed in response to changes in the independent variable.
Purpose: To assess the outcome of the experiment.
Controlled Variables (Constants)
Definition: Variables kept the same throughout the experiment to ensure a fair test.
Purpose: To isolate the effect of the independent variable.
Control Group and Experimental Controls
Control Group Definition: A group of subjects in an experiment that is treated exactly like the experimental group except that it does not receive the variable being tested.
Purpose of a Control Group: Provides a baseline for comparison; helps determine the true effect of the experimental variable; ensures results are due to the variable, not other factors.
Terminology:
Controlled variable: The condition kept constant during an experiment.
Example: No fertilizer was added to any plants in an experiment.
Control Group: The group of subjects exposed to controlled variables but not the experimental variable(s); The group of plants that receives no fertilizer.
The Placebo: A Special Type of Control Group
What is a Placebo? A substance or treatment that looks like a real medical intervention but has no active ingredients (e.g., a sugar pill).
Why Use a Placebo?
Serves as a control group to compare the effects of the actual treatment against no treatment.
The Placebo Effect:
A psychological response where patients feel better simply because they believe they are receiving treatment, even if not.
Key Point: The placebo is a type of control group that helps isolate the true physiological effect of the treatment being tested.
Example: Pain Medicine Experiment (Data Example)
Experimental scenario: Rate pain on a scale of 1–10 with three groups:
Group 1: No medicine → average pain = 7.5
Group 2: Placebo → average pain = 4.5
Group 3: Pain medicine → average pain = 4.2
Question: Based on these data, would you conclude the medicine was effective?
A. Yes, the medicine appears to be effective
B. No, the medicine does not appear to be effective
Answer (interpretation): Because Group 3 shows a lower average pain than Groups 1 and 2, the medicine appears effective, though placebo effects are present in Group 2.
Single-Blind Experiment
Placebo Effect: For a placebo to work, the recipient must believe the treatment is real and effective.
Single-Blind Design: Participants do not know whether they are receiving the actual treatment or a placebo.
Why it matters:
Preserves the integrity of the placebo effect.
Helps isolate the true physiological impact of the treatment.
Reduces participant bias in reporting outcomes.
Double-Blind Experiment
Definition: In a double-blind experiment, neither participants nor the researchers collecting data know who receives the actual treatment or the placebo.
Purpose: Minimizes experimenter bias and supports objective data collection and interpretation.
Why it matters: Double-blind designs are the gold standard in clinical research because they minimize bias from both participants and experimenters.
Discussion Activity: Experimental Design (Leaf Senescence in Maples)
Observation: Maple leaves in Illinois senesce (fall in autumn).
Question: What triggers leaf fall in autumn?
Task: Break into groups (3–5) and brainstorm:
Multiple plausible hypotheses
Multiple possible experiments
Example hypotheses discussed across groups include:
A. Temperature Drop Hypothesis: Cooler temperatures trigger biochemical changes leading to senescence.
B. Daylight Reduction Hypothesis: Shorter days signal onset of senescence.
C. Water Availability Hypothesis: Reduced rainfall/soil moisture contributes to leaf drop.
D. Hormonal Change Hypothesis: Internal plant hormones shift in response to seasonal cues.
E. Genetic Programming Hypothesis: Leaf drop is genetically timed regardless of environment.
F. Other Creative Hypotheses.
Example experiments discussed:
A. Controlled Light Exposure: Vary light duration to test if shorter days trigger senescence.
B. Temperature Manipulation: Use growth chambers to test different temperatures.
C. Water Stress Test: Vary watering to test drought effects.
D. Hormone Application: Apply hormones or inhibitors to leaves.
E. Geographic Comparison: Compare timing across latitudes/elevations.
F. Other Clever Experiments.
Nemoria arizonaria: Seasonal Polymorphism in the Emerald Moth
Species: Nemoria arizonaria lays eggs on oak trees twice a year (early Spring and midsummer).
Early Spring hatchlings feed on oak catkins/flowers and resemble the flowers.
Midsummer hatchlings feed on oak leaves and resemble twigs.
Experimental design steps to test mimicry:
Step 1: Egg Collection – Eggs collected from many females to ensure genetic diversity and rule out individual variation as cause of mimicry.
Step 2: Controlled Rearing – At least one egg from each female placed into 8 identical cups.
Larvae raised under 8 different combinations of temperature, light, and diet conditions.
Each condition had at least 2 larvae per female, ensuring replication.
Three key experimental variables (a 2 × 2 × 2 factorial design):
1) Day Length (Long vs Short)
2) Temperature (Cool vs Warm)
3) Food Source (Spring food: oak flowers/catkins vs Summer food: oak leaves)Factorial design details:
The design includes 2^3 = 8 treatment groups (each combination of the two levels of each of the three factors).
The rows in the study typically map to: [Long Days, Spring-like Temp, Oak Flowers], [Long Days, Spring-like Temp, Oak Leaves], [Long Days, Summer-like Temp, Oak Flowers], [Long Days, Summer-like Temp, Oak Leaves], [Summer-like Days, Spring-like Temp, Oak Flowers], [Summer-like Days, Spring-like Temp, Oak Leaves], [Summer-like Days, Summer-like Temp, Oak Flowers], [Summer-like Days, Summer-like Temp, Oak Leaves].
Question prompts in the slides (e.g., Does this experimental design include a control group?):
Answer: In the provided design, there is no explicit control group with all variables left at baseline; thus, the question would be answered as “No” (B) based on the given setup.
Seasonal Polymorphism: Results and Interpretation (Nemoria)
The aim is to determine how environmental cues interact with development to produce different caterpillar phenotypes (flower-like vs twig-like) for adaptive mimicry.
The three-variable factorial design allows testing of main effects and interactions among day length, temperature, and food source on larval form.
Expected outcomes include potential interactions such as:
Day length × Temperature effects on timing of morph development.
Food source effects on morphology in conjunction with light exposure.
Giraffes: Hypotheses and Experiments on Neck Length
Three broad ideas are often discussed in lay and scientific contexts:
A) Feeding Advantage Hypothesis: Long necks help reach high leaves, especially when lower foliage is scarce.
B) Neck-as-a-Weapon Hypothesis: Long necks aid males in combat (necking) for mating.
C) Thermoregulation Hypothesis: Longer necks help dissipate heat by increasing surface area.
Additional ideas:
D) Vigilance Hypothesis: Height improves predator detection.
E) Sexual Selection Hypothesis: Long necks are attractive to mates, independent of survival advantage.
F) Other creative hypotheses.
Experimental design ideas discussed:
A. Feeding Behavior Study: Observe feeding height relative to season/habitat to see if neck length correlates with feeding height and food availability.
B. Combat Observation: Analyze male giraffe fights to determine if neck length influences success.
C. Heat Dissipation Test: Measure body temperature and heat loss with varying neck lengths under different conditions.
D. Predator Detection Simulation: Use models/virtual environments to test detection range and reaction time.
E. Mate Preference Survey: Study whether females prefer longer-necked males independently of fighting success.
F. Other Clever Experiments.
Data Interpretation: Foraging Patterns and Giraffe Neck Hypothesis
If necks evolved to reach tall vegetation, what foraging patterns would be expected in the wild?
A) Observable tendency to feed at greater heights during dry seasons when lower foliage is scarce.
B) Less reliance on lower vegetation when tall leaves are accessible.
C) Increased bite-size abundance at higher strata during scarcity.
Data interpretation prompts:
Here, the actual distribution of feeding height data would determine whether the hypothesis is supported or refuted.
Question: Do data support the hypothesis that giraffes have long necks to better feed from tall branches?
Answer choices:
A Yes
B No
Multiple Competing Hypotheses (Giraffes)
A. Feeding Advantage Hypothesis
B. Neck-as-a-Weapon Hypothesis
C. Thermoregulation Hypothesis
D. Vigilance Hypothesis
E. Sexual Selection Hypothesis
F. Other Creative Hypotheses
Which of the Following Best Describes a Control Group?
A. A group that receives the experimental treatment and is observed to determine how the variable affects the outcome.
B. A group that is treated the same as the experimental group in every way except it does not receive the variable being tested, allowing for comparison of results.
C. A group that receives a placebo or inactive treatment to simulate the experimental condition without introducing the actual variable.
D. A group that is randomly selected from the population to ensure unbiased representation in the study.
Correct answer: B
Summary and Cross-cutting Themes
The scientific method emphasizes testable hypotheses, controlled comparisons, replicability, and openness to revision.
Distinguish clearly between scientific theories (well-supported explanations) and everyday uses of the word "theory".
Models are simplifications that aid understanding, not perfect replicas; usefulness depends on the insights they provide, not their accuracy in every detail.
Experimental design relies on controlling variables, using independent and dependent variables, and applying appropriate control groups and blinding to minimize bias.
Placebos and blinding are essential tools to separate genuine treatment effects from expectations or observer bias.
Real-world examples (Brazil–Africa fossil distribution, Nemoria arizonaria, giraffe neck hypotheses) illustrate how hypotheses are formed, tested, and interpreted using systematic experimental designs.
Ethical and practical implications include avoiding bias, ensuring replication, and recognizing the limits of our conclusions until supported by robust evidence.
2 imes 2 imes 2 = 8
The Nemoria experiment uses a factorial design with 2^3 = 8 treatment combinations, illustrating how multiple factors and interactions are studied efficiently.