Scientific Method and Experimental Design in Biological Sciences

Scientific Student Learning Outcomes

• Explain science as a process consisting of hypothesis, experimentation, data analysis, and conclusions.

• Identify specific steps in a scientific scenario and determine appropriate next steps.

• Identify proper experimental scientific designs, including the use of appropriate controls and replication.

• Understand the fundamental differences between observational research designs and experimental (controlled) method designs.

• Assign independent and dependent variables based on a given hypothesis and experimental design.

• Evaluate experimental results to determine if a hypothesis is supported.

Foundations of the Scientific Method

• The scientific method is characterized by the following criteria:

  • Empirical: Based on observation or experience rather than theory or pure logic.

  • Objective: Not influenced by personal feelings or opinions.

  • Replicable: Others can repeat the experiment and obtain the same results.

  • Testable: Capable of being tested by experiment or observation.

  • Falsifiable: Capable of being proven false by empirical evidence.

  • Provisional: Open to change or revision based on new evidence.

Steps of Scientific Inquiry

• General scientific inquiry follows a standard, interrelated process:

  1. Observation / Question: Noting a phenomenon and asking a question about why or how it happens.

  2. Research Topic Area: Conducting background research on the subject.

  3. Hypothesis: Forming a causal, testable explanation for the observed event.

  4. Prediction: Making a specific prediction based on the hypothesis (often in an "If… then…" format).

  5. Experiment: Conducting a test to verify or refute the prediction.

  6. Analyze Data: Examining the results of the experiment.

  7. Conclusions: Determining whether the hypothesis is supported or not supported.

     • If the hypothesis is supported, report the results.

     • If the hypothesis is not supported, try again by forming a new hypothesis or refining the experiment.

  8. Report Conclusions: Sharing the findings with the scientific community.

Characteristics of a Hypothesis

• A hypothesis is a suggested explanation for an event that is testable.

• Causal: It suggests a reason $(a \rightarrow b)$ for the occurrence.

• Testable: It must be capable of being evaluated through experimentation.

• Falsifiable: Experimental results must be able to disprove the hypothesis. Disproving a hypothesis is a valid and necessary part of the process.

Application: The Household Scenario

• The scientific method can be applied to everyday logic, such as a malfunctioning appliance:

  • Observation: My toaster doesn't toast my bread.

  • Question: Why doesn't my toaster work?

  • Hypothesis: There is something wrong with the electrical outlet.

  • Prediction: If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.

  • Experiment: I plug my coffeemaker into the outlet.

  • Results: My coffeemaker works. (This implies the hypothesis that the outlet was the problem is not supported).

Case Study: Pesticide Hazards to Amphibians

• Initial Observation: A new pesticide, described as a mitochondrial poison, is being applied to corn fields near areas where frogs and toads live.

• Research Question: Does a hazard to the amphibians exist?

• Hypothesis Generation: One might hypothesize that exposure to the mitochondrial poison pesticide leads to increased mortality in local amphibian populations.

Components of Experimental Design

• Controls:

  • Well-designed experiments must include a control group to serve as a baseline for comparison.

  • The control group should be treated exactly like the experimental replicates, except for the specific factor being tested (e.g., no fungicide application).

  • Sham or Mock Treatment: A control where the subject receives the same handling or a neutral substance (like a water spray) to ensure effects aren't caused by the act of treatment itself.

• Variables:

  • Independent Variable: The factor that is manipulated or changed by the scientist (e.g., the presence or concentration of fungicide).

  • Dependent Variable: The factor that is measured or observed; it responds to the independent variable (e.g., the number of frogs that died).

  • Standardized Variable: Factors held constant for all subjects in an experiment to ensure they do not influence the results (e.g., the species of frog, temperature, humidity).

• Replicates:

  • Replicates are multiple units or individuals within the same treatment group.

  • Importance of Replication: All individuals are slightly different and may respond differently to treatments. Replicates ensure the results represent a whole population or species rather than just one unique individual.

  • Example: Testing 10 frogs in separate containers sprayed with fungicide and 10 frogs in separate containers sprayed with water.

Observational vs. Experimental Research

• Observational Research (Discovery Science):

  • Involves measuring phenomena as they occur naturally in the environment without direct manipulation.

  • Example: Measuring amphibian populations across many farms and comparing them to the fungicide concentrations measured at each location.

• Controlled Experiments:

  • Involves direct manipulation of the environment or subjects to test a specific variable.

  • Example: Exposing amphibians to specific concentrations of fungicide in a lab setting with a negative control.

Field Study Example: Headline AMP Fungicide

• Exposure Assessment: Using open-top metal enclosures and paper samplers in corn fields to measure pesticide drift.

• In-Field Data:

  • Northern Zone: $1.52\,\mu g\,a.i./sq\,cm$

  • Southern Zone: $1.06\,\mu g\,a.i./sq\,cm$

  • Spray Drift Zone showed lower levels, while the Reference Zone showed near zero.

• Mortality Results: Comparing fungicides (Headline, Stratego, Quilt) at different concentrations ($0.1 \times$, $1 \times$, and $10 \times$ label rates).

  • High mortality ($\sim 100\%$) was observed at $1 \times$ and $10 \times$ rates for Headline and Stratego, while the control group (Cont.) showed nearly zero mortality.

  • Asterisks ($*$) on graphs typically denote statistically significant differences from the control.

Interpreting Figures and Graphs

• Definition: A visual summary of data used to identify patterns and test predictions.

• Axes:

  • X-axis: Contains the Independent Variable (the variable manipulated).

  • Y-axis: Contains the Dependent Variable (the variable measured).

  • Patterns: Statistical tests are used to identify patterns. If patterns align with the hypothesis, the hypothesis is supported.

• Hypotheses Types:

  • Null Hypothesis: Proposes that the independent variable has no effect on the dependent variable.

  • Alternative Hypothesis: Proposes that the independent variable does affect the dependent variable.

Exercise: Ant Walking Speed

• Independent Variable: Ant Type (categories of ants).

• Dependent Variable: Walking Speed.

• Standardized Variables (Control Variables): Ant species, ant age, and temperature at the time of measurement.

Exercise: Rotavirus Vaccine Study

• Independent Variable: The dose of the vaccine (or the presence of the vaccine vs. placebo).

• Dependent Variable: The incidence of illness from Rotavirus.

• Placebo: A substance containing no active therapeutic ingredient used as a control group to account for the "placebo effect" and ensure changes are due to the vaccine itself.

• Experimental Design Requirements: This study required both controls (placebo) and replication (testing many babies) to be considered well-designed.

Correlation vs. Causation

• Correlation: A statistical relationship between two variables.

• Causation (Mechanism): One variable directly causes the change in the other.

• Spurious Correlation: When two variables appear related but have no causal link. For example, Google searches for "that is sus" correlates highly with Lululemon's stock price ($r = 0.973$, $p < 0.01$), but there is no mechanical link. Testing enough random variables will eventually lead to coincidentally similar patterns.

Science vs. Pseudoscience

• Science:

  • Must be testable and falsifiable.

  • Relies on empirical data and replicated experiments.

  • Actively welcomes criticism.

  • Is open to revision when new results emerge.

  • Evidence leads to the conclusion.

• Pseudoscience:

  • Is not testable or has not been rigorously tested.

  • Relies on anecdotal evidence or personal beliefs.

  • Shuns criticism.

  • Is unwilling to revise ideas even when contrary evidence exists.

  • Starts with a predetermined conclusion and looks only for supporting evidence.

Questions & Discussion

• Discussion Question: What is the difference between how most people come to conclusions and how scientists (should) come to conclusions?

• Response Summary: Most people often rely on intuition, personal experience, or anecdotal evidence to form conclusions. In contrast, scientists must use the structured scientific method, relying on empirical data, objective testing, and the willingness to discard a hypothesis if the data does not support it.