BHS Methods in Behavioral Sciences I April 11, 2003 Chapter 2 (Stanovich) – Cont. from Wed. Chapter 3 (Ray) – Developing the Hypothesis

Falsifiability  Seeking support for hypotheses commits the logical fallacy of affirming the consequent.  Instead, we must test hypotheses by seeking disconfirmatory evidence.  A testable theory is one that can be proven wrong – if it is wrong. It must have the chance to fail. A theory cannot explain every outcome.

Popper’s Approach  Predictions from theory (hypotheses) must be specific. They must state what will happen and what will not happen. General predictions or all-encompassing predictions cannot be tested (are unfalsifiable).  When data accumulates that contradicts theory, then theory must be changed. Data is not thrown out – explanations are.

Two Hypotheses  Null hypothesis (H 0 ): There will be no difference between treatment and control groups (no treatment effect).  Alternative hypothesis (H 1 ): There will be a difference of a particular kind. Directionality – states how the treatment group will differ from the control group.  We test the null hypothesis and by rejecting it (disconfirming it) can accept the alternative.

The Neyman-Pearson Approach  Two theories can be compared by predicting incompatible outcomes: If theory A is correct, hypothesis A will be confirmed and B will be disconfirmed. If theory B is correct, hypothesis B will be confirmed and A will be disconfirmed.  The comparison is not with the control group ( is there a treatment effect or not) but with the predictions made by the two theories.

Platt’s Strong Inference  Devise alternative hypotheses.  Devise a crucial experiment (or experiments) with alternative possible outcomes, each of which would exclude one or more hypotheses.  Carry out the experiment to get clear results.  Return to step 1, applying the findings to generate a new set of alternative hypotheses.

Errors are Important  We learn something, even when an experiment does not “work” – does not produce the expected result. Knowledge advances when we find that our ideas are wrong and we can abandon incorrect beliefs. We must be willing to let evidence guide belief – not the other way around.  Scientists criticize each other’s ideas in an ongoing dialectic that produces change.

Working on the Fringes  Interesting questions are those that: Exist at the fringes of knowledge. Can be tested using existing methods.  Many questions are important but untestable.  Some questions are interesting to the public but not to scientists because they have already been answered: ESP and other paranormal claims, astrology.

Operational Definitions  A hypothesis redefines a general concept in terms of clearly observable operations that anyone can see and repeat.  Any given concept can be defined in many ways. It is safest to use multiple operational definitions.  Construct validity – is the operational definition appropriate for the concept?

Figure 3.1. (p. 54) From any one global construct, there are several possible operational definitions, depending on the questions asked and the type of population studied.

Measurement  Measurement is the process of assigning numbers to things in the world.  Measurements must be: Accurate Consistent  Reliability refers to the consistency of a measure (test-retest, same result each time).  Validity refers to the accuracy of a measure.