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  1. Ruling out chance as an explanation
  2. The null hypothesis
  3. Steps in hypothesis testing
  4. Why the null hypothesis is not accepted
  5. The precise meaning of the p value
  6. At what level is H0 really rejected?
  7. Statistical and practical significance
  8. Type I and II errors
  9. One- and two-tailed tests
  10. Confidence intervals and hypothesis testing
  11. Following a nonsignificant finding
  12. Exercises

precision consulting

For a practical application of significance testing in business decisions see "An Appreciation of the Role of Statistical Hypotheses in Decision Making" by P.K. Viswanathan.

Instructional Demos
Tests of proportions
by Charles Stanton

Hypothesis testing: Does chance explain the results?
by P. B. Stark

Hypothesis testing part 1, Hypothesis testing part 2
by Keith Dear

Testing statistical hypotheses
by H. J. Newton, J. H. Carroll, N. Wang, and D. Whiting

Hypothesis testing
by David Stockburger

Difference between means: Type I and Type II errors and power
by T. D. V. Swinscow; revised by M. J. Campbell

Hypothesis testing
by Will Hopkins of the University of Otago

Interpreting significant and nonsignificant p values
by Harvey Motulsky, GraphPad Software

On the link between error bars and statistical significance
by Harvey Motulsky, GraphPad Software

Absence of evidence is not evidence of absence
by Douglas G. altman and J. Martin Bland

Probability and Hypothesis Testing (PDF)
by Bruce Weaver

Hypothesis Tests
by StatTrek

Statistics As Principled Argument
by Robert P. Abelson

Concepts and Controversies
by David S. Moore

Forgotten Statistics : A Self-Teaching Refresher Course
by Jeffrey Clark