I. Chapter overview
II. Causality: The simple experiment's purpose
A. The logic of causalityB. Obstacles to establishing causality: The variability problem
C. Solving the variability problem
III. Basic terminology
A. Experimental hypothesisB. Null hypothesis
C. Administering the independent variable
D. Experimental and control groups
1. The value of independenceE. Collecting the dependent variable2. The value of assignment
F. The statistical significance decision
1. Statistically significant resultsG. Summary of the ideal simple experiment2. Null results
IV. Errors in determining whether results are statistically significant
A. Type 1 errors and their preventionB. Type 2 errors
1. Preventing type 2 errors by increasing power
a. Reducing random error
1. Standardize procedures and use reliable measures2. Use a homogeneous group of participants
3. Code data carefully
b. Let random error balance out
c. Create larger effects
C. Summary of the effects of statistical considerations on designing the simple experiment
V. Nonstatistical considerations
A. External validity versus powerB. Construct validity versus power
C. Ethics versus power
D. Ethics versus the simple experiment
VI. Analyzing data from the Simple Experiment: Basic Logic
A. Estimating what you want to know
1. Calculating sample means2. Comparing sample means
B. Inferential statistics: Judging the accuracy of your estimates
1. Estimating the accuracy of individual sample means
a. Consider population variability: The value of the standard deviationb. Consider sample size: The role of standard error
c. Using standard error
2. Estimating the accuracy of your estimate of the difference between population means
a. How differences are distributed: The large sample caseb. How differences are distributed: The small sample case
c. Interpreting results of a t test
d. Assumptions of the t test
C. Questions raised by results
1. Questions raised by nonsignificant results2. Questions raised by significant results
VII. Concluding remarks
Summary
Exercises