Scientific Method and Experimental Design Vocabulary
Living Organisms, Metabolism, and Biological Principles
Energy Intake and Metabolism: Biological systems take in food or some type of energy source. This energy is processed through metabolism to obtain the energy necessary to perform work.
The Concept of Work: In biological and physical contexts, work is defined by the formula:
This energy expenditure allows for movement, such as lifting an arm.
At the microscopic level, it allows neurons in the brain to fire and send signals.
Genetic Blueprint: All living cells possess a genetic blueprint composed of either DNA or RNA.
Gradients and Physical Laws: A concentration gradient exists when there is a high concentration of a substance in one area and a low amount in another.
Naturally, substances move from high-concentration areas to low-concentration areas without external effort.
This principle of gradients underlies biological functions and physical flight (e.g., how airplanes fly).
Form and Function: Biological systems adapt so that the physical form fits the required function. While not always a perfect match, body systems adapt over many years and generations to meet functional needs. If functional needs change, the body can adapt its form accordingly.
Homeostasis: This refers to the ability of an organism to maintain stable internal conditions despite changes in the external environment. An example of this is the regulation of body temperature.
The Scientific Method: Structure and Purpose
Definition: The scientific method is a well-regimented, structured process designed to be repeatable. Without agreed-upon steps, scientific findings cannot be trusted or repeated.
Benefits of the Scientific Method:
Advancing Knowledge: It allows scientists to take an existing body of knowledge and improve it, even through tiny incremental additions.
Trust and Reliability: It provides a "laundry list" of repeatable steps to verify findings.
Fact vs. Fiction: It helps distinguish fact from fiction and cut through misinformation/disinformation in the pursuit of truth.
Societal Advancements: Historically, before the rigorous application of this method, diseases were often attributed to divine punishment. Through the scientific method, germs (bacteria and viruses) were discovered.
Historical Examples: DNA was not identified until after World War II. The method allowed for monumental human achievements such as the moon landing.
Types of Scientific Studies
Observational Studies:
Features: These involve observing things in the natural world without determining which individuals receive a specific treatment.
Groups: They identify trends in existing populations or groups. For instance, comparing the crop growth of a farmer's field in Kansas against a field in Northern Georgia where it rains more significantly.
Ethics: These are common in human studies because it is often immoral or illegal to perform experiments on people (e.g., one cannot ethically expose a person to radiation to see if it causes cancer).
Drawbacks: They can only show correlation (relationships between variables), not causation (cause and effect). One cannot definitively state that the rain in Georgia caused the crop difference because other variables were not manipulated.
Controlled Experiments:
Features: Researchers randomly assign subjects to different treatments and control as many variables as possible.
Purpose: These are considered the "gold standard" because they allow for the determination of cause and effect.
Example (Caffeine and Sleep):
Observational approach: Tracking daily coffee consumption and sleep hours, which might show an inverse relationship.
Controlled approach: Split 100 people into two groups. Half receive a caffeine pill; the other half receive a placebo (sugar pill). If the caffeine group sleeps significantly less, it can be concluded that the caffeine caused the change because it was the only difference between the groups.
The Five Steps of the Scientific Method
1. Observation: Noticing a phenomenon (e.g., Isaac Newton observing an apple falling).
1.5 Question: Asking "Why did this happen?" A well-designed experiment can only test a well-designed question.
2. Hypothesis and Prediction:
Hypothesis: A proposed, causal explanation for the observation (The "Why"). For example: "The apple fell because of an invisible force called gravity."
Prediction: A forecast of what will happen specifically in an experiment if the hypothesis is true. For example: "If I drop 100 objects, they will all fall at the same rate."
3. Experimentation: Designing and running the test to collect data.
4. Data Analysis: Evaluating the collected information (e.g., finding that objects on Earth accelerate at ).
5. Conclusion: Determining if the results support or do not support the hypothesis.
The Cyclical Nature: Science is not a linear list but a cycle. If a conclusion does not support the hypothesis, scientists figure out what happened, develop a new hypothesis, and start the process again.
Criteria for Hypotheses
Testability: Science cannot test every domain. It is limited to the physical world and cannot test morality, personal preference, religion, or supernatural phenomena.
Falsifiability: A hypothesis must be capable of being proven wrong. If it can only be supported, it does not require an experiment.
Non-Exclusivity: Proving one hypothesis correct does not automatically prove all others wrong; it simply means the specific hypothesis tested was supported.
Experimental Variables and Groups
Independent Variable: The specific factor or treatment that is changed or manipulated by the researcher (also called the predictor variable).
Dependent Variable: The factor that is measured to see the response to the independent variable (also called the response variable).
Controlled Variables (Constants): Factors kept exactly the same across all groups to ensure the independent variable is the only thing causing a change (e.g., water, sunshine, temperature, elevation).
Subjects: The units (living or non-living) to which the experiment is applied.
Control Groups:
Negative Control: A group where no treatment (independent variable) is applied. This provides a baseline for comparison.
Positive Control: A group where a treatment is applied to see what a known successful result looks like.
The pGlo Lab Case Study:
Utilizes E. coli bacteria and a piece of DNA (plasmid) containing a gene for Green Fluorescent Protein (GFP).
Scientists agitate and heat the bacteria to make them take up the DNA.
Negative Control 1: Regular bacteria on a growth medium (LB). They should grow normally.
Negative Control 2: Bacteria on a medium with Ampicillin (an antibiotic). They should die unless they successfully took up the plasmid containing resistance genes.
Positive Treatment: Only bacteria with the plasmid and a specific "on/off switch" (arabinose) will glow under UV light.
Minimizing Bias in Research
Bias: When anything other than objective truth influences results.
Intentional Bias: Deliberately skewing results (e.g., an oil company reporting that natural gas causes climate change but oil does not). This is rare in published research due to conflict-of-interest disclosures.
Sample Bias: Non-random sampling. For example, if a researcher concludes that taller people are more educated because they only sampled tall PhD holders in one section of a room.
Confirmation Bias: The unintentional human tendency to favor information that matches current beliefs or the desired outcome of the hypothesis.
Placebo Effect: A physiological change that occurs simply because a subject believes they are receiving a treatment, even if they are only receiving a sugar pill.
Solutions to Bias:
Randomization: Subjects must be randomly assigned to treatment groups to ensure errors are distributed evenly.
Sample Size and Replicates: A replicate is a subject receiving the same treatment.
Rule of Thumb: A minimum of replicates is often required for statistical reliability (based on the Central Limit Theorem).
Blinding:
Single-Blinding: Subjects do not know if they are receiving the drug or the placebo.
Double-Blinding (The Gold Standard): Neither the subjects nor the researchers know who is receiving which treatment until the data analysis phase. This prevents researchers from treating subjects differently based on their group.
Repeatability and Ethics
Repeatability: An experiment must be repeatable by different scientists. If ten different researchers get the same result, the findings gain credibility.
Ethics in Science: Historically, horrific experiments like the WWII doctors' trials and the Tuskegee Syphilis Study (where subjects were harmed or denied treatment) highlighted the need for ethical oversight.
Regulatory Boards:
Institutional Review Board (IRB): Oversees ethical considerations for research involving human subjects.
Institutional Animal Care and Use Committee (IACUC): Oversees ethics for research involving vertebrates (animals with backbones).
Minimization of Pain: When using vertebrates, scientists must minimize pain. For example, fish used in heavy metal studies (e.g., Oklahoma mining pond research) must be anesthetized in a specific water solution before being sacrificed for gill analysis.
Questions & Discussion
Question: How can researchers minimize the effects of the placebo effect and confirmation bias?
Answer: By using blinding. Specifically, double-blinding is the most effective method because it keeps both the subject and the researcher from knowing which treatment is being administered.
Question: Why is sample size so important?
Answer: Small samples can lead to false conclusions based on random chance. Increasing the sample size (ideally above ) ensures that the results are more representative of the actual population.
Question: Does a supported hypothesis prove every other possibility wrong?
Answer: No. It simply provides evidence and reasoning for that specific explanation. Other hypotheses may still exist or be supported by different data.