BIOL1113 Introductory Biology - Vocabulary Flashcards

What is Life?

  • 5 Characteristics of Life:
    • Life is organized
    • Life requires energy
    • Life maintains internal consistency
    • Life reproduces, grows, and develops
    • Life evolves
  • In-Class Prompt: Is a virus alive?
    • A virus is an infectious microbe consisting of a segment of nucleic acid (either DNA or RNA) surrounded by a protein coat.
    • A virus cannot replicate alone; it must infect cells and use components of the host cell to make copies of itself. (NIH)

The Scientific Method

  • The scientific method is a series of interrelated steps that generally follow a standard process. See Figure 1.10.
  • Key idea: all steps are interrelated and data-driven.

Observations and Questions

  • Observations come from what we see, hear, smell, read, etc.
  • Questions build on existing knowledge.
  • Finding connections between otherwise unrelated observations can greatly advance science.

Hypothesis and Predictions

  • A hypothesis is a tentative explanation for observations—a testable idea of how to answer a question.
  • Predictions allow you to test the hypothesis in a controlled experiment.
  • Example framing: if a condition X changes, then outcome Y should occur.

Data and Conclusions

  • Scientists use experimental data to draw conclusions about the hypothesis.
  • Data can either support or falsify a hypothesis.

Publish and Review

  • When there is enough data to convincingly support or falsify a hypothesis, scientists submit a manuscript (perhaps prior to completion).
  • Submissions are reviewed by experts in the field (peer review).
  • If accepted, findings are published in a peer-reviewed journal.

Types of Science

  • Observational Research (Discovery Science): collect data without manipulating the system.
  • Controlled Experiments: manipulate one or more variables while keeping others constant.
  • Consider strengths/weaknesses of each approach and when one is more appropriate than the other.
  • Example context: Figure 1.11 contrasts observational vs. experimental approaches.

Data Trends and Correlation Example

  • A slide shows Google searches for 'that is sus' correlating with Lululemon stock price (LULU).
  • Reported values: correlation coefficient r = 0.973, p < 0.01 (years 2008–2023; data visualized from 2008 to 2023).
  • Takeaway: correlation does not imply causation; be cautious about interpreting relationships.

Example to Practice: Pesticide Hazard to Amphibians

  • Scenario: A pesticide that is a mitochondrial poison is applied to corn; frogs and toads live nearby. Does a hazard exist for amphibians?
  • Steps: Hypothesis → Design Experiment.
Potential Experiments
  • A. Controlled experiment: Expose amphibians to varying pesticide concentrations, include a negative control (no pesticide). Measure harm.
  • B. Observational study: Measure amphibian populations across landscapes and compare to expected fungicide concentrations.
  • C. Field study: Select locations with/without fungicide treatment; place amphibians in the field and monitor health.
  • D. Exposure assessment: Measure pesticide penetration into leaves at random locations using devices placed on top, below the canopy, and near field locations.
  • In-Field Exposure Assessment example graph shows Pyraclostrobin concentration in
    • \( ext{µg/a.i./cm}^2\)) with values such as Southern: \(1.06\ \mu g\ a.i./cm^2)\, Northern: \(1.52\ \mu g\ a.i./cm^2)\.
  • Open-top enclosures and field samplers can be deployed prior to treatment (Figure depicts an enclosure in the drift area).

Components of Experiments: Variables

  • Independent variable: what is manipulated (e.g., type of coffee bean). Expression: \(X\).
  • Dependent variable: what is measured (e.g., amount of caffeine). Expression: \(Y\).
  • Standardized (controlled) variable: held constant for all subjects (e.g., mass of beans). Expression: \(Z\).
  • Visual reference: Figure 1.11.

Controls

  • Control group: baseline for comparison (e.g., beans treated with only water).
  • Experimental groups: may or may not show different results from control (e.g., beans with extra fertilizers).

Replicates

  • Replicates: multiple units/individuals within the same treatment group.
  • Rationale: individuals vary; replication helps the results represent a population.
  • Discussion point: replicates can be challenging in some setups (e.g., fish tanks).

Interpreting Figures

What is a figure or graph?

  • A visual summary of data.
  • Helps identify patterns, test hypotheses/predictions.

Common types of figures

  • Bar graph:
    • Independent variable has distinct treatment groups.
    • Plot summary statistics (mean or median) with a measure of variation (e.g., standard deviation or standard error).
  • Regression:
    • Tests the relationship between an independent variable and a dependent variable.
    • Indicates if relationship is positive, negative, or none.
    • Data points are individual units; often includes a regression line.

What makes a good figure or graph?

  • Clearly labeled axes with units.
  • Simple, straightforward organization.
  • Include summary statistics (mean/median).
  • Show some measure of error (e.g.,
    • Standard deviation: \(s\)
    • Standard error: \(SE = \frac{s}{\sqrt{n}}\)
  • Include a regression line when appropriate.
  • The figure should summarize results and help evaluate hypotheses/predictions.

Interpreting a figure – Important points

  • Identify the independent variable (manipulated) on the x-axis and the dependent variable on the y-axis.
  • Identify the hypothesis being tested; state null hypothesis \(H0\) and alternative hypothesis \(Ha\).
  • Look for patterns; statistical tests help reveal patterns; if not clear, use best judgment.
  • Compare data patterns to the hypothesis.

Exercise: Interpreting a Figure (Ant Type Study)

  • Question 1: What is the independent variable?
    • Answer: Ant Type (A: Minor caste; B: Media caste; C: Major caster; etc.).
  • Question 2: What is the dependent variable?
    • Answer: Walking Speed.
  • Question 3: What are some control variables?
    • Hint: They will not be on the figure: Ant species, Ant age, Temperature when measurement is taken.
  • Source: Ant Type Tross et al. 2022. J. Exp. Biol.

Independent vs Dependent (More on Variables)

  • Independent variable: Ant Type (as above) on the x-axis.
  • Dependent variable: Walking Speed on the y-axis.

Hypotheses for Figure Studies

  • Potential null hypothesis: Ant Type has no effect on Walking Speed.
  • Potential alternative hypothesis: Ant Type affects Walking Speed.
  • Reference: Ant Type Tross et al., 2022. J. Exp. Biol.

Interpreting Figures – Describing Patterns

  • Describe how the dependent variable changes with changes in the independent variable.
  • Do results support the null or the alternative hypothesis? Explain why.

Discussion and Recall

  • Active Discussion: 2 minutes to discuss how to remember where to find dependent and independent variables on graphs.
  • iClicker Question: Where do you find the independent variable on a graph?
    • A) Y-axis
    • B) In the title
    • C) X-axis
    • D) At the intersection of the X and Y axis
    • E) Z-axis
    • Correct answer: C) X-axis

Practice: Vaccine and Rotavirus (Babies) Example

  • Task: Determine the dependent variable (DV) and independent variable (IV).
  • Question: What is a placebo and why is it used?
  • Context: Babies were given a vaccine to test how it affected illness from Rotavirus.

Science vs Pseudoscience

What is science?

  • Testable and falsifiable
  • Based on empirical data and replicated experiments
  • Welcomes criticism
  • Open to revision based on new results
  • Uses evidence to determine a conclusion

What is pseudoscience?

  • Not testable or not tested, or not subject to empirical testing
  • Based on anecdotal evidence or beliefs
  • Shuns criticism
  • Unwilling to revise ideas or consider contrary evidence
  • Often starts with a conclusion and searches for evidence to support it
  • Source: Scientific American discussion on drawing the line between science and pseudo-science

Pseudoscience Example: Dowsing

  • Dowsing uses sticks or metal rods to locate underground water; observer signals when rods move.
  • Classic question: Does dowsing work?
  • An actual experiment (Martin, 1984) tested dowsing with a controlled setup:
    • Platform with 4 pipes under it; water randomly pumped through one pipe in each trial; 40 trials total.
    • An experienced dowser attempted to locate the pipe with flowing water.
    • Expected under random chance: 10 correct out of 40.
    • Observed: 9 out of 40 correct.
  • Conclusion (scientific): Dowsing does not work; no better than random chance.
  • Pseudoscientific conclusion: The dowser claimed the experiment was not serious and would work under real conditions.
    • Quote reflects the contrast between evidence-based conclusions and belief-based conclusions.

Cryptozoology

  • Definition: cryptozoology (krip-tǝ-zö-ä-lǝ-jē) – the study of and search for animals (often legendary or disputed) to evaluate their existence.
  • Question: Is cryptozoology science? Why or why not?
  • Reference: Discussion and definitions; linked article for context.

In-Class Challenge and Review

In-Class Challenge

  • Task: Propose a hypothesis to test and design an experiment to test your hypothesis.
  • Observation to start: A new pesticide that is a mitochondrial poison is being applied to corn. There are frogs and toads living in and near the corn field. Is there a hazard to the amphibians?
  • BIOL1113: Introductory Biology

What is Life? The Scientific Method – Recap

  • Revisit the connection between life concepts, the scientific method, and critical thinking about science vs pseudoscience.
  • Emphasize: testability, replication, evidence, and willingness to revise conclusions.

Video Review

  • A video provides a review of the scientific method (link: viddler) for additional contextual understanding.

Key Formulas and Variables (Summary)

  • Independent variable: \(X\)

  • Dependent variable: \(Y\)

  • Standardized variable: \(Z\)

  • Mean: \(\bar{x}\)

  • Standard deviation: \(s\)

  • Standard error: \(SE = \frac{s}{\sqrt{n}}\)

  • Correlation coefficient: \(r\) (e.g., (r = 0.973))

  • p-value: \(p\) (e.g., (p < 0.01\))

  • Concentration unit example: (\mu g\ a.i./cm^2\) (micrograms of active ingredient per square centimeter)

  • Hypotheses notation:

    • Null hypothesis: \(H_0: \text{There is no effect of the independent variable on the dependent variable.}\)
    • Alternative hypothesis: \(H_a: \text{There is an effect.}\)

  • Note: Figures and data should clearly label axes with units, present summary statistics, and show error measures. Regression lines are used to summarize relationships where appropriate.