GEP Investigation: Scientific Investigation Notes

GEP Investigation: Scientific Investigation Notes

• This set of notes summarizes the content from the provided transcript, organized as comprehensive study notes that cover the scientific method, hypothesis writing, data presentation, and group-based lab procedures.
• All key terms, procedures, examples, and guidelines from the transcript are included with explanations, examples, and practical applications.

GEP Investigation Level Learning Outcomes

• Identify the basic taxonomy and principles of the scientific method as it pertains to the natural, physical world.
• Infer relationships, make predictions, and solve problems based on analysis of evidence or scientific information.

Student Learning Outcomes

• Apply the scientific method to analyze novel problems.
• Recognize the parts of an experiment.
• Generate hypotheses and formulate predictions based on those hypotheses.
• Choose an appropriate visual representation for scientific data.
• Interpret data in graphical form.
• Identify variables in an experiment such as independent, dependent and control groups.
• Choose an appropriate visual representation for scientific data. (Note: repeated in the transcript; purpose remains the same.)
• Apply understanding of graphical interpretations to real world data.

What is Science? Descriptive science vs hypothesis-based science

• Science relies on collecting and interpreting data (qualitative and quantitative) to refute or support ideas.
• Descriptive science collects patterns and generalizes using logic (e.g., plants need light to live).
• Hypothesis-based science (the scientific method) proposes explanations (hypotheses), makes predictions, and tests them with data.
• The goal is to evaluate hypotheses, not to prove them; hypotheses can be rejected, modified, or supported by data.
• Example analogy: walking into a dark room and flipping a switch—possible hypotheses include issues with the bulb, power outage, wiring, or breaker status. Each hypothesis must be testable and falsifiable.
• Key terms introduced: hypothesis, testable, falsifiable.
• Deductive reasoning is used to form an if-then statement: If [independent variable] is related to [dependent variable], then [prediction] because [biological mechanism].
• Scientific inquiry begins with curiosity and is an iterative process of observation, hypothesis, testing, and revision.

Step-by-step Walkthrough: SEA BUTTERFLY Scientific Explanation (C-E-R)

• Background visuals included (sea butterfly Ciona antarctica; amphipod Hyperiella dilatata; Antarctic toothfish predator).
• Discussion focuses on writing a scientific explanation using the Claim–Evidence–Reasoning (CER) framework.
• Step 1: Make Observations
  • Amphipods carry sea butterflies on their backs.
  • Amphipods who lose their sea butterfly quickly find another.
  • Amphipods with sea butterflies move more slowly than those without.
• Step 2: Develop a Question
  • If amphipods are slower when carrying a sea butterfly, making them more vulnerable to predators, then why do amphipods carry sea butterflies?
• Step 3: Conduct Background Research & Develop a Hypothesis
  • Identify Variables: Independent (I) and Dependent (D).
  • Formulate how the independent variable influences the dependent variable.
  • Rationale (BECAUSE) ties the hypothesis to a biological concept.
• Step 4: Conduct a Controlled Experiment
  • Measure survival rates over four generations (illustrative setup).
• Step 5: Collect Data & Graph
  • Report observations such as predation on uncoupled amphipods and on sea-butterfly-coupled amphipods.
• Step 6: Interpret Data & Draw Conclusions
  • Learn to read graphs: identify X vs Y axes (IV vs DV), caption interpretation, describe trends left-to-right, note notable features, consider sample size, and beware that correlation does not imply causation.
• Step 7: Justify your Conclusions (C-E-R)
  • Consider supporting experiments and cite outside sources as justification.
• Conceptual takeaway: Writing CERs is central to communicating scientific conclusions and linking data to claims with reasoning.

Graphs, Tables, and Data Presentation: Guidelines and Tips

• Data synthesis often involves graphical/tables to visualize trends.
• Common graph types:
  • Line graphs and scatter plots for continuous numeric data (independent variable on X-axis, dependent on Y-axis).
  • Bar graphs for categorical data (one variable categorical, the other numeric).
  • Pie charts for proportional data (no axes; use a legend).
• Graph labeling conventions:
  • Axes labeled with variable names and units.
  • Figures labeled as Figure # with a descriptive caption below the graph; no separate title required.
  • Tables labeled with Table # and a descriptive caption above the table.
• Example from sea butterfly data:
  • Table 2: Data for a bar graph (categorical IV: uncoupled vs coupled amphipods; numeric DV: survival counts).
  • Table 3: Survival over 4 generations (Time (days) vs Uncoupled vs Coupled).
  • Graphs should reflect data type: bar graph for categorical vs numeric, line/XY scatter graph for continuous IV vs DV.
• Graphs should be designed to clearly show trends and allow interpretation of patterns and potential hypotheses.

Sea Butterfly Experiment: A Comprehensive Walkthrough

• Step 1: Make Observations
  • Amphipods carry sea butterflies; carrying affects speed of movement.
• Step 2: Develop a Question
  • Why do amphipods carry sea butterflies if it slows them down and could increase predation risk?
• Step 3: Hypothesis Development & Variables
  • Hypothesis example: If amphipods carrying sea butterflies increases survival, THEN amphipods carrying sea butterflies will survive at greater rates compared to those without, BECAUSE sea butterflies provide chemical protection against predation.
  • Variables:
  • Independent (I): Presence of a sea butterfly
  • Dependent (D): Survival rate
  • Rationale links to a chemical mutualism concept.
• Step 4: Conduct a Controlled Experiment
  • Design: measure survival rates across generations under different treatments (uncoupled vs coupled sea butterflies).
• Step 5: Collect Data & Graph
  • Observations include prey-predator interactions: fish preyed on uncoupled amphipods; fish rejected solitary sea butterflies; fish rejected amphipod coupled with sea butterfly.
• Step 6: Interpret Data & Draw Conclusions
  • How to read data: same as Step 6 above; identify IV and DV, interpret trends, consider whether the data supports the hypothesis.
• Step 7: Justify Conclusions
  • Integrate additional sources and context to support or refine the conclusion.
• Additional notes include the importance of testable & falsifiable hypotheses and the contrast between descriptive science and hypothesis-based science.

Writing Hypotheses: Structure and Rationale

• Standard format for a hypothesis with predictions:
  • If [Independent Variable] is related to [Dependent Variable], THEN we predict [Prediction] BECAUSE [Biological Mechanism].
  • Example: If amphipods carrying sea butterflies increases survival, THEN amphipods carrying sea butterflies will survive at greater rates compared to those that do not, BECAUSE sea butterflies provide chemical protection against predation.
• Important distinctions:
  • Not all questions produce a testable hypothesis.
  • Hypotheses should be falsifiable; there must exist possible observations that could refute them.
  • A test that could falsify the hypothesis is considered a strong test.
• How to construct a good hypothesis:
  • Include a clear independent variable, dependent variable, and a mechanism for why the relationship exists.
  • Use the if-then-because form when possible.
• Example discussion: lamp not turning on due to plug vs bulb vs power outage vs wiring—each alternative is a hypothesis that can be tested and potentially falsified.

Independent, Dependent, and Controlled Variables; Replication and Controls

• Independent Variable (IV): The variable deliberately changed in the experimental group.
• Dependent Variable (DV): The measured outcome.
• Controlled Variables: Variables kept constant to avoid confounding effects.
• Controls:
  • Control Group: Does not receive the experimental manipulation.
• Why single IV per experiment? Reduces confounding effects and makes it easier to attribute changes in DV to changes in IV.
• Levels of treatment: The numeric or categorical value set for the IV in the experimental condition.
• Replication: Repeating the procedure multiple times to ensure results are not due to chance. Replication outcomes are averaged or analyzed statistically.
• The importance of a well-defined hypothesis, procedure, and control of variables is emphasized throughout the lab preparation and execution.

Data Presentation: Qualitative vs Quantitative, Tables and Figures

• Qualitative data describe qualities or characteristics; quantitative data are numerical.
• Tables summarize data; figures/graphs visualize trends and relationships.
• Guidelines for data organization:
  • Tables: Columns represent comparable results; rows represent treatments; include units; descriptive captions above.
  • Figures: Include a figure number and a descriptive caption below; no title above needed.
• Mary’s dragonfly example illustrates different graph types:
  • Table 6: Average flight height by dragonfly weight class.
  • Figure 1: Column graph showing average flight height across weight classes.
  • Figure 2: XY scatter graph showing the relationship between dragonfly weight (IV) and average flight height (DV).
• The takeaway: Different representations can reveal different relationships; choose the graph type based on data characteristics (continuous vs categorical, etc.).

Practical Example: Mary’s Dragonflies Data (Illustrative Tables and Figures)

• Table 6 (Qualitative/Quantitative data):

  • Dragonfly weight (g) | Number sampled | Average flight height (m)
  • 0-1.9 | 20 | 16.81
  • 2-3.9 | 21 | 12.13
  • 4-5.9 | 21 | 9.62

• Figure 1: Column graph – Average flight height for dragonflies in different weight classes.

• Figure 2: XY scatter graph – Relationship between Weight Class (IV) and Average Flight Height (DV).

• Interpretation: As dragonfly weight increases, average flight height tends to decrease; relationship appears roughly linear up to about 4 g, then weaker at higher weights.

• Additional graph notes:

  • Always label axes with units, ensure readability, and provide a descriptive caption.
  • Consider alternative representations to gain new insights (e.g., linear vs nonlinear trends).

Experimental Application: Muscle Contraction Lab (Science Practice)

• Essential question: Does temperature affect muscle contraction? Does fatigue affect muscle contraction?
• Experimental design involves two experiments:
  • Experiment 1: The effect of temperature on muscle contraction (inquiry).
  • Experiment 2 (cookbook): The effect of fatigue on muscle contraction.
• Group roles (rotating weekly): Facilitator, Materials Manager, Data Analyst, Quality Controller.
• Steps to structure experiments:
  • Define Hypothesis: If temperature/fatigue changes, then predicted change in contraction rate or total contractions.
  • Identify IV, DV, and controls.
  • Plan data collection: number of contractions in a minute, rate, duration, etc.
  • Record data in a preformatted data sheet (e.g., Table 7 for Experiment 1).
  • Graph data in Excel (x-axis: time or temperature; y-axis: contractions or rate).
  • Create a CER (Claim-Evidence-Reasoning) statement for the data.
• Notes on data representation:
  • Graphs must have x- and y-axis labels with units; figures are labeled as Fig. # with a caption underneath.
  • Tables have a title above the data with a descriptive caption.
• Procedure excerpt for fatigue experiment:
  • Two participants measure contractions per minute, then continue contracting for another minute, repeat for a total of 4 minutes, rest, repeat.
• Example data table (Table 8) needs to include time intervals and contractions per trial with averages.

Data Quality, Analysis, and CER Bridge

• Data quality checks require each group member to assess whether data align with the hypothesis and identify potential sources of error.
• The Data-to-Conclusion Bridge: Each group member articulates a one-sentence claim–evidence–reasoning (CER) statement before writing the conclusion.
  • Claim: What happened.
  • Evidence: What data support it.
  • Reasoning: Why that makes sense biologically.
• Role Reflection: Each member explains one decision or action they were responsible for during the lab to promote accountability.
• Peer Evaluation: End-of-lab evaluation of group members’ contributions.
• The rubric (Table 4) for the scientific explanation assesses HYPOTHESIS, CONCLUSION, and EVIDENCE with criteria ranging from missing/unsatisfactory to well-structured and well-supported by data.
• The lab emphasizes that science is testable, falsifiable, repeatable, observable, quantifiable, peer-reviewed, self-correcting, and constantly changing.

Important Definitions and Concepts (Key Terms)

• Hypothesis: A tentative explanation for an observation or question.
• Testable: A hypothesis can be supported or rejected by carefully designed experiments or observations.
• Falsifiable: A hypothesis that can be shown to be false by evidence.
• Independent Variable (IV): The factor deliberately changed in an experiment.
• Dependent Variable (DV): The measured outcome.
• Controlled Variables: Variables kept constant to avoid affecting the outcome.
• Control Group: The group that does not experience the experimental manipulation.
• Replication: Repeating the experiment to ensure reliability.
• CER: Claim-Evidence-Reasoning framework for concluding based on data.
• DescriptiveScience vs Hypothesis-Based Science: Descriptive science catalogs patterns; hypothesis-based science seeks explanations and predictions tested by data.
• Correlation vs Causation: Correlation does not imply causation; experiments aim to establish causal relationships.
• Data presentation: Tables summarize data; Figures (graphs) visualize data; both must be properly labeled with captions.

Practice Scenarios and Practice Questions (Summary)

• Mary’s dragonflies and flight height vs weight class: Develop hypothesis, IV, DV, and controlled variables; determine how to falsify the hypothesis.
• Alex’s petunia study: Examine the relationship between water amount and flower count; design treatment groups with increasing water levels; determine the hypothesis and variables.
• For both, identify:
  • Hypothesis
  • Independent variable
  • Dependent variable
  • Controlled variables
  • Controls
  • How hypothesis could be falsified

Quick Reference: Common Formats and Conventions

• If [Independent Variable] is related to [Dependent Variable] THEN we predict [Prediction] BECAUSE [Biological Mechanism].
• Hypotheses should be testable and falsifiable; predictions should be measurable.
• The scientific process is iterative: a hypothesis may be supported, revised, or rejected based on data; nothing is ever “proved.”
• Data are best understood through multiple representations (tables, bar graphs, line graphs, XY scatter plots, pie charts).
• Graphs and tables must include descriptive captions and labels, and axes should include units.
• When writing conclusions, use CER to connect data to claims with reasoning; cite outside sources when justifying conclusions.

Example equations and formats you should be able to reproduce in exams:

• If A (IV) is related to B (DV) THEN we predict C BECAUSE D.
• ext{If } A ext{ (IV)} ext{ is related to } B ext{ (DV)}, ext{ THEN } ext{Prediction} ext{ because } ext{Mechanism}.

End of Notes