Overview
This lecture explores how psychological scientists evaluate claims using scientific reasoning, contrasting it with everyday thinking, and discusses the qualities of trustworthy scientific theories and data.
Scientific vs. Everyday Reasoning
- Scientific and everyday reasoning both use induction—drawing general conclusions from specific observations.
- Scientific claims use cautious, probability-based language, while everyday statements are often more absolute.
- Anecdotal evidence is limited by memory and sample bias, while scientific data aims to be systematic and representative.
- A key feature of scientific theories is falsifiability—they must be testable and able to be proven wrong.
Key Features of Scientific Theories
- Good scientific theories are accurate, consistent, broad in scope, simple, and fruitful for new discoveries.
- Falsifiability distinguishes science from pseudoscience or untestable claims.
- Scientific knowledge is always open to revision with new evidence.
Scientific Reasoning and Interpretation
- Scientific conclusions are based on the probability, not absolute proof, due to limited samples and complexity.
- Inductive reasoning leads to probable conclusions; deductive reasoning leads to certain conclusions if premises are true.
- Null-hypothesis significance testing (NHST) assesses whether observed data differs from what would be expected by chance.
Errors and Probability in Research
- Type I error: falsely rejecting a true null hypothesis (false positive).
- Type II error: failing to reject a false null hypothesis (false negative).
- Probability values (p-values) indicate the likelihood of results occurring by chance.
Science as a Social and Value-Laden Activity
- Science is influenced by personal and cultural values, making total objectivity unattainable.
- Facts (objective data) and values (beliefs about what should be) both shape scientific inquiry.
- Using multiple levels of analysis helps understand complex phenomena in psychology.
Scientific Theories and Revision
- Scientific theories are built from many studies and aim to describe, explain, and predict phenomena.
- Theories are refined or replaced as new data emerge, as shown by shifts like the heliocentric model in astronomy.
Science in Context
- Science, while systematic and reliable, has limitations; it works best alongside other ways of knowing.
- Understanding scientific methods enhances critical evaluation of information and helps generalize findings.
Key Terms & Definitions
- Anecdotal evidence — Biased, personal evidence not necessarily reliable.
- Causality — When one variable directly causes another.
- Correlation — Statistical measure of the relationship between variables.
- Data — Systematically collected information for analysis.
- Deductive reasoning — Drawing specific conclusions from general principles.
- Empirical — Based on observation or measurable evidence.
- Falsify — Ability to be tested and potentially disproven.
- Generalize — Extending findings to broader groups or settings.
- Hypothesis — A testable, tentative explanation.
- Inductive reasoning — Inferring general conclusions from specific observations.
- Null-hypothesis significance testing (NHST) — Testing if observed effects differ from chance.
- Objective — Free from personal bias.
- Population — Whole group under study.
- Probability — Measure of certainty for event occurrence.
- Pseudoscience — Claims presented as science but lacking scientific validity.
- Sample — Subset of a population for study.
- Scientific theory — Well-supported, testable explanation of phenomena.
- Type I error — False positive.
- Type II error — False negative.
- Value — Belief about how things should be.
Action Items / Next Steps
- Review the Three-Step Method for learning support (link).
- Create practice questions from the module content to deepen understanding.
- Complete any assigned readings or practice assessments for this module.