V.  Illusion of Cause

    A.  Brief Introduction to Scientific Reasoning

       1.  Scientific Methodologies

            c.  Correlational Method

      2.  Issues Common to All

            b.  Validity

      3.  Overview of the Experimental Method

                  i.  Empirical

                  ii.  Theory Driven

                            2)  Popper's Falsification Approach

                            3)  Combining the Two:  Competitive Hypothesis Testing

            b.  Status of Results

                  i.  Existence Proof

                  ii.  Assessing Relative Importance/Contributions

            c.  Central Methodological Feature:  Manipulation

                  ii.  Dependent Variable

                  iii.  Control Condition

            d.  Simple Designs

                  ii.  Within-S

                  iii.  Some Advantages/Disadvantages of Each

            e.  Speaking of Disadvantages & Validity...

                  ii.  Confounding:  Alternative Explanations

                  iii.  Inappropriate Auxiliary Assumptions

                  iv.  Experimenter Bias (Double-Blind Procedure)

                  v.  Inappropriate Assumptions for Statistical Analysis

      4.  Statistical Issues

                  i.  Descriptive

                  ii.  Inferential

            b.  Inferential Statistics & Reliability

                  i.  From the Sample Group to the Population it comes from

                  ii.  Between Groups (e.g., experimental & Control)

                  iii.  Issue of Variability

                  iv. Solution:  Given Certain Assumptions, Try to establish Chance Probability of Result

                            1)  Is Result So Rare as to be Unlikely?

                            2)  If So, Accept;  If Not, Reject Manipulation Being Responsible

                            3)  So, Find a Suitable Definition of Rare:  alpha-level

                            4)  Determine Actual Observed Probability: p-level

                            5)  What are Conventional alpha levels?

                            6)  Note Implications for a TradeOff:  Type I vs. Type II Error

            c.  More on Statistical Testing

                  i.  Directional vs. Non-Directional Hypotheses

                  ii.  Parametric vs. Non-Pametric Tests

                  iii.  Common Statistics/Tests You'll Come Across

                 iv.  Conservative Testing Bias

      5.  Complex Experiments

            b.  Interactions vs. Additive Effects

            c.  Sometimes Misleading Readers by Reporting Both...

      6.  Can a Theory be Too Powerful?

    B.  Causality & Coherence

      1.  Causal Learning in Classical Conditioning

      2.  Causality in Classifying Mental Disorders:  Ahn & Kim

            c.  Ahn & Kim start by collecting Psychologists' theories of symptom causality...

      3.  Causality & Classifiation

       4.  Causality & Interest:  Watson's Sudy of 3-Month-Old Infants

       5.  Causality & Reading

            c.  Myers & Duffy

     C.  The Illusion of Cause

       1.  Three Mechanisms

            c.  Causal Schemas:  post hoc, ergo propter hoc fallacy

       2.  Selective Matching

            c.  Fugelsang & Thompson:  Prior Beliefs

       3.  Correlation & Causation (just reminding you!)

       4.  Causal Schemas

            c.  Hannigan & Reinitz:  Inferential Errors

            e.  Superstitous Behavior (Humans & Animals)

            g.  MMR Vaccinations & Autism:  Andrew Wakefield & Jenny McCarthy