Metrics As Scores can be thought of as an interactive, multiple analysis of variance (abbr. "ANOVA," Chambers et al., 2017). An ANOVA might be used to estimate the goodness-of-fit of a statistical model. Beyond ANOVA, which is used to analyze the differences among hypothesized group means for a single quantity (feature), Metrics As Scores seeks to answer the question of whether a sample of a certain feature is more or less common across groups. This approach to data visualization and -exploration has been used previously (e.g., Jiang etal., 2022). Beyond this, Metrics As Scores can determine what might constitute a good/bad, acceptable/alarming, or common/extreme value, and how distant the sample is from that value, for each group. This is expressed in terms of a percentile (a standardized scale of [0, 1]), which we call score. Considering all available features among the existing groups furthermore allows the user to assess how different the groups are from each other, or whether they are indistinguishable from one another. The name Metrics As Scores was derived from its initial application: examining differences of software metrics across application domains (Hönel et al., 2022). A software metric is an aggregation of one or more raw features according to some well-defined standard, method, or calculation. In software processes, such aggregations are often counts of events or certain properties (Florac & Carleton, 1999). However, without the aggregation that is done in a quality model, raw data (samples) and software metrics are rarely of great value to analysts and decision-makers. This is because quality models are conceived to establish a connection between software metrics and certain quality goals (Kaner & Bond, 2004). It is, therefore, difficult to answer the question "is my metric value good?". With Metrics As Scores we present an approach that, given some ideal value, can transform any sample into a score, given a sample of sufficiently many relevant values. While such ideal values for software metrics were previously attempted to be derived from, e.g., experience or surveys (Benlarbi et al., 2000), benchmarks (Alves et al., 2010), or by setting practical values (Grady, 1992), with Metrics As Scores we suggest deriving ideal values additionally in non-parametric, statistical ways. To do so, data first needs to be captured in a relevant context (group). A feature value might be good in one context, while it is less so in another. Therefore, we suggest generalizing and contextualizing the approach taken by Ulan et al. (2021), in which a score is defined to always have a range of [0, 1] and linear behavior. This means that scores can now also be compared and that a fixed increment in any score is equally valuable among scores. This is not the case for raw features, otherwise. Metrics As Scores consists of a tool- and analysis suite and an interactive application that allows researchers to explore and understand differences in scores across groups. The operationalization of features as scores lies in gathering values that are context-specific (group-typical), determining an ideal value non-parametrically or by user preference, and then transforming the observed values into distances. Metrics As Scores enables this procedure by unifying the way of obtaining probability densities/masses and conducting appropriate statistical tests. More than 120 different parametric distributions (approx. 20 of which are discrete) are fitted through a common interface. Those distributions are part of the scipy package for the Python programming language, which Metrics As Scores makes extensive use of (Virtanen et al., 2020). While fitting continuous distributions is straightforward using maximum likelihood estimation, many discrete distributions have integral parameters. For these, Metrics As Scores solves a mixed-variable global optimization problem using a genetic algorithm in pymoo (Blank& Deb, 2020). Additionally to that, empirical distributions (continuous and discrete) and smooth approximate kernel density estimates are available. Applicable statistical tests for assessing the goodness-of-fit are automatically performed. These tests are used to select some best-fitting random variable in the interactive web application. As an application written in Python, Metrics As Scores is made available as a package that is installable using the PythonPackage Index (PyPI): pip install metrics-as-scores. As such, the application can be used in a stand-alone manner and does not require additional packages, such as a web server or third-party libraries.