screen with replicates, we can directly estimate variability for each compound; as a consequence, we should use SSMD or t-statistic that does not rely on the strong assumption that the z-score and z*-score rely on. One issue with the use of t-statistic and associated p-values is that they are affected by both sample size and effect size. They come from testing for no mean difference, and thus are not designed to measure the size of compound effects. For hit selection, the major interest is the size of effect in a tested compound. SSMD directly assesses the size of effects. SSMD has also been shown to be better than other commonly used effect sizes. The population value of SSMD is comparable across experiments and, thus, we can use the same cutoff for the population value of SSMD to measure the size of compound effects . Techniques for increased throughput and efficiency Unique distributions of compounds across one or many plates can be employed either to increase the number of assays per plate or to reduce the variance of assay results, or both. The simplifying assumption made in this approach is that any N compounds in the same well will not typically interact with each other, or the assay target, in a manner that fundamentally changes the ability of the assay to detect true hits. For example, imagine a plate wherein compound A is in wells 1-2-3, compound B is in wells 2-3-4, and compound C is in wells 3-4-5. In an assay of this plate against a given target, a hit in wells 2, 3, and 4 would indicate that compound B is the most likely agent, while also providing three measurements of compound B's efficacy against the specified target. Commercial applications of this approach involve combinations in which no two compounds ever share more than one well, to reduce the (second-order) possibility of interference between pairs of compounds being screened. Recent advances Automation and low volume assay formats were leveraged by scientists at the NIH Chemical Genomics Center (NCGC) to develop quantitative HTS (qHTS), a paradigm to pharmacologically profile large chemical libraries through the generation of full concentration-response relationships for each compound. With accompanying curve fitting and cheminformatics software qHTS data yields half maximal effective concentration (EC50), maximal response, Hill coefficient (nH) for the entire library enabling the assessment of nascent structure activity relationships (SAR). In March 2010, research was