Free tool · Data & statistics
Statistical test chooser
Up to five questions about your data, then the test to use, its non-parametric alternative and why.
Answer up to five short questions about your study and this tool names the statistical test to use, its non-parametric alternative, and the reason it fits. It covers the ten tests that appear in most Indian PhD theses, from the t-test and ANOVA to chi-square, correlation and regression.
Use it when you’re planning the analysis chapter, or when a DC member asks why you chose a test and you want to check your reasoning.
Method
How this tool works
Three things decide the test. First, your question: are you comparing groups, looking for a relationship, or predicting an outcome? Second, the type of outcome: continuous (marks, income, a scale score), categorical (yes/no, district) or ordinal (a single Likert item). Third, the design: how many groups you have, and whether the same people were measured more than once. The guide explains these as the three questions.
The tool asks those questions one at a time and skips the ones that don’t apply. Its answers follow the decision table in our guide, row for row.
Each answer gives a parametric test and a non-parametric alternative. Parametric tests, like the t-test and ANOVA, assume roughly normal data within groups. With reasonably large, similar-sized groups they cope with moderate non-normality. Non-parametric tests, like Mann-Whitney U and Kruskal-Wallis, work on ranks and suit clearly skewed data, small samples and ordinal outcomes.
Limits
What this tool can’t do
FAQ
Frequently asked questions
t-test or ANOVA?
A t-test for two groups, ANOVA for three or more. With exactly two groups they give the same answer. For two independent groups, prefer Welch’s t-test, which doesn’t assume equal variances.
My data aren’t normal. What now?
Don’t switch to a non-parametric test automatically. With reasonably large, similar-sized groups, t-tests and ANOVA tolerate moderate non-normality. Look at the plots, check for outliers, consider the sample size, and justify the choice in writing. Use the non-parametric alternative when the data are clearly skewed, the samples are small, or the outcome is ordinal.
Is a Likert item continuous?
A single item is ordinal, so describe it with frequencies and the median and compare groups with Mann-Whitney U or Kruskal-Wallis. A scale score, the sum or mean of several items measuring one construct, is usually treated as continuous once its reliability has been checked. The guide’s section on Likert data has more detail.
Do I need SEM?
Only if your model has latent constructs measured by several items, with paths between them that must be tested together. Many theses use SEM because it looks advanced when regression would answer the question. Choose it because the model needs it.
Which software should I use?
Any standard package, as long as you use the right test and report it properly. SPSS is common in Indian universities, and R, jamovi and JASP are free. Name the software and its version in your methodology chapter.
Keep reading
Related guides and services
Choosing the right statistical test
Match your question and data type to the right test, including non-parametric options.
Sample size calculator
Cochran’s formula, the small-population correction and the regression rules of thumb, with every step shown so you can defend the number.
Statistical Analysis (SPSS, R, AMOS, SmartPLS)
The right tests, assumptions checked, results interpreted — and syntax you can rerun and defend.
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