Empiricism Course

Lesson 1 • Core Principles of Empiricism

Examines the key tenets: sensory experience as the source of knowledge, fallibilism, and revisability. Students can articulate each principle with examples.

Lesson 2 • Empirical vs. Non-Empirical Claims

Teaches students to classify statements by their empirical status using clear criteria. Directly prepares students for evidence evaluation in later chapters.

Lesson 3 • Historical Origins of Empirical Thought

Traces empiricism from ancient observation-based inquiry through early modern philosophy. Provides context for why empirical methods became dominant in science.

Lesson 1 • Evaluating Evidence Quality

Introduces criteria for assessing reliability, validity, and relevance of collected evidence. Students apply a quality checklist to real observational datasets.

Lesson 2 • Designing Structured Observations

Covers protocols for systematic, repeatable observation across field and lab settings. Students design an observation plan with defined variables and recording methods.

Lesson 3 • Types of Empirical Evidence

Distinguishes qualitative, quantitative, primary, and secondary evidence and their appropriate uses. Prepares students to select evidence types suited to specific research questions.

Lesson 4 • The Nature of Observation

Examines how observation is theory-laden and subject to perceptual bias. Grounds students in the epistemological complexity of seemingly simple data collection.

Lesson 1 • Null and Alternative Hypotheses

Explains the logic of null hypothesis significance testing and its empirical role. Students construct null and alternative pairs for given research questions.

Lesson 2 • Iterating on Hypotheses

Covers how failed tests refine rather than end inquiry, reinforcing fallibilism. Students revise hypotheses based on simulated negative results.

Lesson 3 • From Observation to Hypothesis

Shows how patterns in observations generate candidate explanations. Students practice moving from raw data to clearly stated, testable propositions.

Lesson 4 • Criteria for a Good Hypothesis

Covers falsifiability, specificity, parsimony, and predictive power as evaluative standards. Students score sample hypotheses against each criterion.

Lesson 1 • Integrating Reasoning Modes

Shows how induction, deduction, and abduction work together in a complete empirical inquiry cycle. Students map reasoning modes onto a real research workflow.

Lesson 2 • Inductive Reasoning Fundamentals

Explains how general conclusions are drawn from specific observations and the limits of induction. Students evaluate the strength of inductive arguments.

Lesson 3 • Common Reasoning Fallacies

Identifies fallacies that corrupt empirical reasoning, including hasty generalization and affirming the consequent. Students detect and correct fallacies in case studies.

Lesson 4 • Deductive Reasoning Fundamentals

Covers valid argument forms and how deduction derives necessary conclusions from premises. Students test argument validity using standard logical forms.

Lesson 1 • Principles of Experimental Control

Explains control groups, baseline conditions, and the logic of holding variables constant. Establishes the conceptual foundation for all design decisions in the chapter.

Lesson 2 • Measurement and Operationalization

Addresses how abstract constructs are translated into measurable variables with precision. Students operationalize three constructs for a given research question.

Lesson 3 • External Validity and Generalizability

Examines threats to external validity and strategies for designing generalizable studies. Students critique a published design for generalizability limitations.

Lesson 4 • Randomization and Blinding

Covers random assignment, random sampling, and single and double blinding to reduce bias. Students apply randomization procedures to a sample design scenario.

Lesson 1 • Identifying Patterns and Relationships

Teaches correlation, regression basics, and pattern detection in multivariate data. Students distinguish genuine patterns from noise using statistical criteria.

Lesson 2 • Inferential Statistics Essentials

Introduces hypothesis testing, confidence intervals, and p-values as inferential tools. Students interpret inferential outputs and link them to hypothesis decisions.

Lesson 3 • Descriptive Statistics for Empiricists

Covers measures of central tendency, spread, and distribution shape as tools for summarizing data. Students compute and interpret descriptive statistics for a provided dataset.

Lesson 4 • Interpreting Results Empirically

Guides students in translating statistical outputs into empirically grounded conclusions. Emphasizes epistemic humility and the limits of data-based inference.

Lesson 1 • Observational Causal Methods

Teaches methods for causal inference without randomization, including matching and instrumental variables. Students select appropriate methods for non-experimental datasets.

Lesson 2 • The Correlation-Causation Distinction

Clarifies why correlation does not imply causation and the conditions required for causal claims. Students analyze datasets to separate associative from causal signals.

Lesson 3 • Limits of Causal Claims

Examines epistemic boundaries of causal inference and the role of replication in strengthening claims. Students write a calibrated causal conclusion for a complex dataset.

Lesson 4 • Establishing Causal Evidence

Covers criteria for causal evidence including temporal precedence, mechanism, and dose-response. Students evaluate a case study against each causal criterion.

Lesson 1 • Appraising Individual Studies

Provides a systematic framework for evaluating study design, methodology, and reporting quality. Students apply an appraisal checklist to three contrasting study types.

Lesson 2 • Narrative and Qualitative Synthesis

Teaches structured narrative synthesis for bodies of evidence that resist quantitative pooling. Students produce a thematic synthesis from a set of qualitative studies.

Lesson 3 • Translating Evidence into Practice

Guides students in converting synthesized evidence into actionable recommendations with appropriate caveats. Students draft a practice recommendation with an evidence grade.

Lesson 4 • Meta-Analysis Fundamentals

Introduces pooling effect sizes across studies and interpreting heterogeneity statistics. Students interpret a forest plot and identify sources of between-study variation.