The Faecal Egg Count Reduction Test (FECRT) is the most widely used method of assessing the efficacy of anthelmintics in large animals. Faecal Egg Count (FEC) data are frequently charatcerised by a low mean, high variability, and with equine data small sample sizes and frequent zero observations. Accurate analysis of the data therefore depends on the use of an appropriate statistical technique. Analyses of simulated FECRT data by methods based on cal- culation of the empirical mean and variance, non-parametric bootstrapping, and a novel Markov chain Monte Carlo (McMC) technique are compared. The McMC technique consistently outper- formed the other methods, independently of the distribution from which the data were gener- ated. 95% confidence intervals produced by non-parametric bootstrapping contained the correct value between only 10% and 16% of the time, compared to between 3% and 8% of the time for McMC. Computationally intensive techniques such as McMC should therefore be used for analysis of these kinds of data in order to avoid making erroneous inference of the true effi- cacy of anthelmintics in the field. Software to implement the novel McMC technique is freely available in the form of an add-on package to the R statistical programming language from http://cran.r-project.org/web/packages/bayescount/index.html.