Abstract The problem of multiple comparisons is met with in many clinical trials, epidemiological studies, or public health studies, in which case, data fishing is a possibility. The aim of this essay is to provide a brief yet, a comprehensive review on the problem of multiple comparisons, and how data fishing risks public health studies’ outcomes. Introduction Public health or medical research centers on an input to output relationship. The aim is to examine whether input (explanatory) variables relate to the effect (output or outcome) variables. Alternatively, the purpose may be to test the null hypothesis (results are not because of chance), that is testing if the effect is only because of the input variables. As an example testing the link between obesity and diabetes can be a cause effect relationship (obesity is a cause for diabetes) or the relationship between obesity (expressed by weight) and diabetes (expressed by blood glucose level). Confounding (confusing) factors may shadow either cases, in the previous example, age or gender can be confounding factors. Therefore, in data analysis, it is essential to identify the variables as input, output, or confounding (Campbell, 2006). The problem of multiple comparisons Testing the null hypothesis serves to guard against unjustifiable conclusions. Testing one hypothesis (effect of a drug A to control hypertension) is primary analysis, occasionally, researchers use data obtained from the study population to examine multiple outcome variables (secondary analysis). Multiple comparisons means testing more than one hypothesis, in other words it is comparing two study groups for more than one output (outcome). Failure to use appropriate statistical methods weakens conclusions (Curran-Everett, 2000). There are two inherent problems on using multiple comparisons, first is the false positive result that is higher probability of detecting non-existing positive outcome, which is a procedural problem (type I error). Second is the false negative that is the limited trial power to detect true treatment effects on secondary analysis, which is a problem, related to sample size (type II error). Therefore, concern about multiple comparisons health or trial studies arise if they are conducted with logical error as data mining, or data fishing (Lord and others, 2004). Examples of multiple comparisons in clinical trials and public heath studies include studying multiple effects (outcomes), multiple treatment comparisons (drugs A, and B to control hypertension). Examples also include subgroups analysis to detect treatment differences, study of prognostic factors, repeated outcome measures overtime, and provisional analysis for treatment effects during various stages of a trial study (Lord and others, 2004). Data fishing Data fishing (dredging or mining) is the inapt search for statistically significant relationships in a data set whether intentional or unintentional because of badly chosen procedure. In this case, the assumed statistical significance is false, that is significance tests do not protect conclusions against data fishing (Roddick and others, 2003). Smith and Ebrahim (2002, pp. 1437-1438) describe an example of data fishing, the first is about the relationship between cancer breast, alcohol and tobacco consumption published in the lancet (October, 2002) t