English (US)

Purpose & User Intent: This tool analyzes numeric data to produce a UEQ-based assessment and core statistical metrics. It accepts structured inputs such as sample size (n), mean (μ), and either standard deviation (σ) or variance (s²); optional inputs include known population SD, confidence level, and a weight vector for case-matching. It validates numeric types, handles missing values through exclusion or simple imputation, and outputs a cohesive results set suitable for formal reporting. It targets researchers, UX analysts, product teams, and QA practitioners performing quick evaluations of experiments or studies.
Core algorithms and outputs: If SD is provided, the tool computes standard error, confidence interval (CI) using z or t methods depending on variance knowledge and sample size; when SD is not provided, it uses sample SE estimates. It returns descriptive metrics (mean, SD, SE, CI) and an UEQ-like composite score, plus an optional effect size (Cohen's d) for paired or independent comparisons if group data is supplied. Bootstrapping can be enabled for non-parametric CI estimation.
Differentiation and use cases: The primary differentiator is transparent, auditable formulas and built-in data quality safeguards, enabling consistent cross-project comparisons. Users can weight observations, align outputs to standard reporting units, and export results to dashboards or documents. Typical use cases include usability experiments, feature evaluations, A/B validation, and QA data reviews. The tool favors clarity, reproducibility, and speed over opaque, ad hoc calculations.