Chapter 1 Notes: Process of Science
Learning Objectives
- Understand how the scientific method is used to test hypotheses.
- Know the factors that influence the strength of the conclusions of scientific studies.
- Know the factors that influence whether the results of any given study are applicable to a particular population of humans.
- Know how to evaluate the evidence in media reports of scientific studies.
- Know how the scientific process can help us make important decisions about human health.
Definitions & Main Ideas
- Anecdotal evidence: evidence derived from personal observations and experiences.
- Non-scientific reasons for discrepancies in reports of scientific studies:
- miscommunication
- misrepresentation
- lack of understanding in the field of study
- Scientific evidence: evidence derived from systematic and controlled experimentation.
- Reasons scientific conclusions are modified over time (examples):
- scientists ask different questions
- improved technology
- better evidence
- experimentation
- articles reviewed by experts before publishing (peer review)
- Science:
- systematic study of the natural world using observations and experiments, with findings often published after peer review.
- Peer-reviewed: articles reviewed by experts in the field before publication.
- Hypothesis: an explanation created from prior studies/observations that can be tested (a testable scientific statement).
- Testable and falsifiable:
- Testable: a hypothesis can be accepted or rejected based on testing/experimentation.
- Falsifiable: there exist possible observations that could rule out the hypothesis.
- Experiment: a controlled, carefully designed test.
- Experimental group vs. control group:
- Experimental group: receives the experimental intervention.
- Control group: receives no experimental intervention (or a standard/placebo treatment).
- Independent vs. dependent variables:
- Independent variable: the factor that is intentionally changed/manipulated (in the experimental group).
- Dependent variable: the outcome measured (the result of the experiment).
- Example of independent and dependent variables:
- Independent: water given to a group of plants.
- Dependent: height of the plants.
- Note: both variables may be present in control and experimental groups depending on design.
- Placebo effect:
- the phenomenon where participants improve or show measured effects after receiving a fake treatment, due to expectation; the control group is given a placebo to mimic the experimental condition.
- Blind study:
- Participants do not know which treatment they are receiving.
- Double-blind study:
- Neither participants nor researchers know which treatment is being administered.
- Statistical significance:
- a mathematical measure of the confidence that results are real and not due to random chance.
- commonly associated with a confidence level greater than 95%, i.e., ext{confidence} > 0.95 ext{ or } p < 0.05.
- Ways to strengthen confidence in study results:
- Replication/repetition by multiple scientists and labs.
- Larger sample size (more experimental subjects) to increase statistical power and robustness.
- Consistent findings across independent studies.
- Personal theory vs. scientific theory:
- Scientific theory: a hypothesis that continues to be supported after years of testing and evidence; broadly applicable explanations.
- Personal theory: an individual belief about how things work that may not have been subjected to rigorous testing or broad support.
- Epidemiology:
- the study of the distribution and determinants of diseases and health-related patterns in populations; includes identifying risk factors.
- Correlation:
- a consistent relationship between two variables, but not necessarily a cause-and-effect relationship (correlation ≠ causation).
- Factors that may affect a scientific study:
- Demographic info (ethnicity, sex, age, height, weight)
- Genetics, diet, health, exercise, environment, substance use/abuse
- Randomized clinical trials (RCTs):
- controlled medical experiments in which participants are randomly assigned to receive a treatment or not; often employ blinding to reduce bias.
The Scientific Method: Steps & Core Concepts
- Five steps of the scientific process/method:
- Observe
- Hypothesize
- Experimentation
- Analyze
- Conclude
- Define science-related terms:
- Science is the systematic pursuit of knowledge about the natural world through observations and experiments, with findings refined through peer review and replication.
- Hypothesis: a testable explanation or educated guess that can be investigated via experiments and observations.
- Experiment: a controlled test designed to isolate and measure the effects of an independent variable on a dependent variable.
- Peer review: critical evaluation by experts before publication to ensure methodological soundness and validity of conclusions.
- Testability and falsifiability: a theory must be testable with the possibility of being disproven by evidence.
- Statistical significance: probability that observed effects are not due to chance; commonly associated with a threshold such as p<0.05 or confidence > 0.95.
Experimental Design & Validity
- Experimental vs control groups:
- Experimental group receives the intervention.
- Control group does not receive the intervention (or receives a standard treatment/placebo).
- Independent vs dependent variables:
- Independent variable is deliberately changed to test its effects.
- Dependent variable is measured to assess the outcome of the intervention.
- Placebo effect and blinding:
- Placebo effect can influence outcomes; placebo controls help isolate treatment effects.
- Blind study: participants do not know which treatment they receive.
- Double-blind study: neither participants nor researchers know which treatment is being administered.
- Testable and falsifiable concepts:
- A hypothesis must be testable; it must also be falsifiable (there must be observations that could disprove it).
Measurements, Significance, & Confidence
- Statistical significance:
- A measure of how likely results are not due to random chance.
- Common target: confidence > 0.95 or p < 0.05.
- Repetition and replication:
- Conducting more experiments and having different scientists replicate findings strengthens confidence.
- Effects of sample size and subject variability:
- Larger samples typically yield more reliable estimates and clearer significance signals.
Interpretation, Theory, and Real-World Relevance
- Epidemiology and correlation:
- Epidemiology studies disease patterns and risk factors in populations; correlations describe associations, not necessarily causation.
- When applying findings to populations:
- Consider demographic and individual factors (ethnicity, sex, age, height, weight), genetics, lifestyle, diet, health status, environment, and substance use.
- Media reports and evidence:
- Be cautious of miscommunication or misinterpretation; seek peer-reviewed sources and understand limitations before applying findings to health decisions.
Randomized Clinical Trials (RCTs)
- Definition and design:
- A randomized clinical trial is a controlled medical experiment where participants are randomly assigned to receive a treatment or not.
- Randomization minimizes selection bias and helps establish causal inferences between interventions and outcomes.
- Common practices:
- Blinding (single, double) to reduce bias in treatment administration and outcome assessment.
- The process emphasizes:
- Systematic observation and testing
- Distinguishing correlation from causation
- The importance of replication, sample size, and methodological rigor
- Critical thinking when evaluating health claims and media reports
- Statistical significance threshold: p<0.05 (or confidence >0.95)
- Relationship concept: correlation does not imply causation
- Replication and power considerations: larger sample sizes increase power to detect true effects
Real-World Implications
- Understanding these concepts helps in:
- Evaluating health claims in the media
- Making informed decisions about personal health based on scientific evidence
- Recognizing the limits of studies and the role of population applicability