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Not every statistics problem needs a small existential crisis. The Original Matter Stats Test Advisor helps psychology and social science students narrow down suitable statistical tests based on study design, data type, and analysis goal. It is built to reduce guesswork, speed up decision-making, and give you a clearer starting point before you commit to an analysis.

Turn group means, standard deviations, and sample sizes into cleaner descriptive write-up support without manually piecing the wording together each time. The Original Matter Descriptive Stats Interpreter helps psychology and social science students generate APA-style descriptive summaries, compare groups at a basic level, and get a clearer sense of what the sample is doing before moving on to inferential tests.

Run chi-squared tests for independence or goodness of fit without having to piece the whole thing together by hand. The Original Matter Chi-Squared Calculator helps psychology and social science students organise observed and expected counts, check the main output clearly, and generate a cleaner APA-style write-up for drafts and assignments.

Estimate the minimum sample size needed for common psychology and social science analyses using effect size, statistical power, and alpha. The Original Matter Sample Size Calculator gives you a cleaner starting point for planning studies, assignments, and methods sections without manually wrestling the calculation every time.

Power analysis is one of those things that sounds simple right up until you have to make an actual decision with it. The Original Matter Effect Size + Power Visualizer helps psychology and social science students explore how effect size, alpha, desired power, and sample size interact for common t-tests. It gives you a clearer visual sense of what your assumptions imply before you commit to a design.

Linear regression has a habit of looking straightforward right up until the assumptions start misbehaving. The Original Matter Regression Assumption Checker helps psychology and social science students work through the main regression assumptions step by step, flag likely issues, and turn that into a clearer summary and write-up support.