The use of standard reference materials (SRMs) such as the National Institute of Standards and Technology (NIST) SRM 1950 has allowed for thorough investigations and interlaboratory comparisons of the implications of sample preparation, data collection, and data analysis. Similar steps have not been taken for all matrices, including model organisms such as C. elegans, resulting in interlaboratory differences that make datasets difficult, if not impossible, to compare. Recent methods such as the Iterative Batch Averaging MeThod (IBAT) provide paths forward to generate RMs for model organisms, allowing for similar comparisons to be made. Analytical methods have traditionally focused on a relatively small set of known molecules and have been optimized in an individual matrix using a targeted approach. This methodology is not feasible for untargeted metabolomics studies, which aim to comprehensively measure all metabolites within a given system. Given the large number and diverse physio-chemical properties of metabolites, no single extraction method currently can capture the entirety of the metabolome. Therefore, matrix-specific sample preparation and analytical methodologies tailored for the chemical classes of interest and their polarity are required. To extend metabolome coverage beyond the limitations of a single extraction, separate split or sequential orthogonal extraction methods can be carried. Sequential and bi-phasic extractions provide increased throughput and metabolome coverage by allowing for the analysis of polar and non-polar species from a single sample; however, the impact of these paired extractions must be investigated to address questions such as metabolite overlap and extraction efficiencies. These experimental enhancements require additional optimization steps and unique approaches to understand these effects. To quantify these steps, appropriate metrics are needed. The most direct method is to use detected features where the presence/absence of unique and overlapping features are compared between differing conditions. Alternatively, when comparing a set of consistently detected features, comparisons between the normalized intensity of each feature can provide insights into the effect of different parameters. There have been major recent efforts to detect biologically relevant features without the need for upfront compound identification or sacrificing the coverage of unknowns for exploratory analysis. Traditional experimental optimization methods such as the one-factor-at-time (OFAT) approach remain a popular choice for conducting such comparisons. In this approach, the influence of the tested factors is measured by changing the level of one factor while the level of all other factors remain constant. Single-factor methods are intensive in terms of time, manual labor, consumable costs, and downstream analysis. In addition, such univariate approaches do not allow investigators to consider the possibility of interactions between the study factors, which could be critical as the complexity of a study increases (for example, a sequential extraction). For these reasons, the application of the OFAT approach is often insufficient and may lead to false or incomplete conclusions, despite its widespread use. Design of Experiments (DOE) is an alternative approach where the influence of each factor is measured simultaneously. This allows for interactions between the factors to be considered and more precisely represents the design space compared to the OFAT approach. The DOE approach is a tool for systematically investigating the impact of each factor on the studied response(s). This allows for the determination of the optimal levels relative to the average response. A wide variety of DOE models exist depending on the experimental goals and practical limitations, includingbut not limited to-fractional factorials, response surface curves, and Taguchi array designs.