Publication Date:
2018-06-15
Description:
Hypothesis testing is a basic tool of epidemiology: outcomes in individuals are apparently random until we apply some form of averaging, and statistics provides us with the sharpest tools for studying populations without being misled by chance. Hypothesis testing is at the very base of understanding, along with modelling and parameter estimation. An article in this issue by Lenters et al 1 uses simulation to address some questions which should be well understood in the epidemiology community, but sadly are not. Any test is characterised by two parameters. Sensitivity: the probability of a positive conclusion given a specified real effect. In the context of hypothesis testing, this is called power. Specificity: the probability of a negative conclusion given there is no real effect. For hypothesis testing, we call (1–specificity) the type 1 error. The interpretation of a positive or negative test result depends not only on...
Print ISSN:
1351-0711
Electronic ISSN:
1470-7926
Topics:
Medicine
