Choosing an appropriate sample size is a common problem and should be given due consideration in any research proposal, as an inadequate sample size invariably leads to wasted resources.
This course gives a practical introduction to sample size determination in the context of some commonly used significance tests.
Examples from a scientific background are used to highlight the problems associated with sample size determination and suggest potential solutions.
Formulae and algebraic notation are kept to a minimum.
The concepts of significance and power in relation to hypothesis testing. Introduction to the role of power in sample size determination.
Continuous outcomes: comparing a mean to a target. One-sample t-test. Introduction to the -power- command and its options.
Continuous outcomes: comparing two means. Two-sample t-test for independent samples and for paired samples.
Binary outcomes: comparing a proportion to a target. One-sample z-test.
Binary outcomes: comparing two proportions. Two-sample z-test for independent samples. McNemar’s test for paired samples.
Delegates are assumed to be Stata users and to be familiar with the concepts of statistical hypothesis testing in relation to means and proportions.
Julious SA (2009) Sample size for clinical trials. CRC Press.
Chow, S-C., Shao, J., Wang, H. and Lokhnygina, Y. (2018). Sample size calculations in clinical research. 3rd edition. CRC Press.
The number of delegates is restricted. Please register early to guarantee your place.