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1. Introduction
2. The basics of hypothesis testing
3. Robustness of the Two-sample t-test
4. Adding data increases the Type I error rate: optional stopping
5. ANOVA can be extremely conservative
6. ANOVA handles only one type of multiple testing problem
7. Power analyses should consider all relevant tests
8. The only p-value you can plan for is zero
9. Subjects and trials do not trade off evenly
10. Replication is a poor way to control Type I error
11. Identifying improper methods through excess success
12. Preregistration may be useful but is not necessary for good science
13. Hypothesis testing is a variation of signal detection theory
14. Using signal detection theory to analyze reported results of hypothesis testing
15. Conclusions.