# Scientific Method Core Principles

## Fundamental Principles

### 1. Empiricism

- Knowledge derives from observable, measurable evidence
- Claims must be testable through observation or experiment
- Subjective experience alone is insufficient for scientific conclusions

### 2. Falsifiability (Popper's Criterion)

- A hypothesis must be capable of being proven false
- Unfalsifiable claims are not scientific (e.g., "invisible, undetectable forces")
- Good hypotheses make specific, testable predictions

### 3. Reproducibility

- Results must be replicable by independent researchers
- Methods must be described with sufficient detail for replication
- Single studies are rarely definitive; replication strengthens confidence

### 4. Parsimony (Occam's Razor)

- Prefer simpler explanations over complex ones when both fit the data
- Don't multiply entities unnecessarily
- Extraordinary claims require extraordinary evidence

### 5. Systematic Observation

- Use standardized, rigorous methods
- Control for confounding variables
- Minimize observer bias through blinding and protocols

## The Scientific Process

### 1. Question Formation

- Identify a specific, answerable question
- Ensure the question is within the scope of scientific inquiry
- Consider whether current methods can address the question

### 2. Literature Review

- Survey existing knowledge
- Identify gaps and contradictions
- Build on previous work rather than reinventing

### 3. Hypothesis Development

- State a clear, testable prediction
- Define variables operationally
- Specify the expected relationship between variables

### 4. Experimental Design

- Choose appropriate methodology
- Identify independent and dependent variables
- Control confounding variables
- Select appropriate sample size and population
- Plan statistical analyses in advance

### 5. Data Collection

- Follow protocols consistently
- Record all observations, including unexpected results
- Maintain detailed lab notebooks or data logs
- Use validated measurement instruments

### 6. Analysis

- Apply appropriate statistical methods
- Test assumptions of statistical tests
- Consider effect size, not just significance
- Look for alternative explanations

### 7. Interpretation

- Distinguish between correlation and causation
- Acknowledge limitations
- Consider alternative interpretations
- Avoid overgeneralizing beyond the data

### 8. Communication

- Report methods transparently
- Include negative results
- Acknowledge conflicts of interest
- Make data and code available when possible

## Critical Evaluation Criteria

### When Reviewing Scientific Work, Ask:

**Validity Questions:**

- Does the study measure what it claims to measure?
- Are the methods appropriate for the research question?
- Were controls adequate?
- Could confounding variables explain the results?

**Reliability Questions:**

- Are measurements consistent?
- Would the study produce similar results if repeated?
- Are inter-rater reliability and measurement precision reported?

**Generalizability Questions:**

- Is the sample representative of the target population?
- Are the conditions realistic or artificial?
- Do the results apply beyond the specific context?

**Statistical Questions:**

- Is the sample size adequate for the analysis?
- Are the statistical tests appropriate?
- Are effect sizes reported alongside p-values?
- Were multiple comparisons corrected?

**Logical Questions:**

- Do the conclusions follow from the data?
- Are alternative explanations considered?
- Are causal claims supported by the study design?
- Are limitations acknowledged?

## Red Flags in Scientific Claims

1. **Cherry-picking data** - Highlighting only supporting evidence
2. **Moving goalposts** - Changing predictions after seeing results
3. **Ad hoc hypotheses** - Adding explanations to rescue a failed prediction
4. **Appeal to authority** - "Expert X says" without evidence
5. **Anecdotal evidence** - Relying on personal stories over systematic data
6. **Correlation implies causation** - Confusing association with causality
7. **Post hoc rationalization** - Explaining results after the fact without prediction
8. **Ignoring base rates** - Not considering prior probability
9. **Confirmation bias** - Seeking only evidence that supports beliefs
10. **Publication bias** - Only positive results get published

## Standards for Causal Inference

### Bradford Hill Criteria (adapted)

1. **Strength** - Strong associations are more likely causal
2. **Consistency** - Repeated observations by different researchers
3. **Specificity** - Specific outcomes from specific causes
4. **Temporality** - Cause precedes effect (essential)
5. **Biological gradient** - Dose-response relationship
6. **Plausibility** - Coherent with existing knowledge
7. **Coherence** - Consistent with other evidence
8. **Experiment** - Experimental evidence supports causation
9. **Analogy** - Similar cause-effect relationships exist

### Establishing Causation Requires:

- Temporal precedence (cause before effect)
- Covariation (cause and effect correlate)
- Elimination of alternative explanations
- Ideally: experimental manipulation showing cause produces effect

## Peer Review and Scientific Consensus

### Understanding Peer Review

- Filters obvious errors but isn't perfect
- Reviewers can miss problems or have biases
- Published ≠ proven; it means "passed initial scrutiny"
- Retraction mechanisms exist for flawed papers

### Scientific Consensus

- Emerges from convergence of multiple independent lines of evidence
- Consensus can change with new evidence
- Individual studies rarely overturn consensus
- Consider the weight of evidence, not individual papers

## Open Science Principles

### Transparency Practices

- Preregistration of hypotheses and methods
- Open data sharing
- Open-source code
- Preprints for rapid dissemination
- Registered reports (peer review before data collection)

### Why Transparency Matters

- Reduces publication bias
- Enables verification
- Prevents p-hacking and HARKing (Hypothesizing After Results are Known)
- Accelerates scientific progress
