Your panel will ask why you used that test. Have the answer ready.
| 1 | 1.1 | |
|---|---|---|
| 2 | 10.6 | |
| 3 | 43.9 | |
| 4 | 35.6 | |
| 5 | 8.9 |
“I review lessons daily”, five-point scale, n = 180.
Students with stronger study habits earned higher averages.
Pearson correlationSpearman rank correlationnormality of study habits rejected, p = .005
rs(178) = .68, p < .001
§1 What a report is made of
Selecting a part shows what produces it. Nothing in the list is written by a language model; the prose is generated from the computed results and then checked against them.
- 01Rules engine · deterministic
The chosen test
Measurement level, number of groups and design decide it. Asked to “correlate gender and civil status”, the engine refuses a correlation — both are categorical — and runs chi-square instead, saying so.
- 02Checked per analysis
The assumption check
Shapiro-Wilk and Levene run on your data. A failure changes the test rather than being noted and ignored, and the substitution is printed with the p-value that caused it.
- 03scipy · statsmodels
The statistic and its size
The test statistic, degrees of freedom, exact p-value, and an effect size with a plain magnitude word, because significance alone does not tell a reader whether the finding matters.
- 04Generated, then validated
The plain-language finding
What the result means for someone who does not read statistics, set beside the exact APA statement for the record.
- 05Numeric-claim validator
The traced figures
Every number in the prose is matched back to a computed result. One that cannot be traced is flagged rather than published.
- 06Exported with the report
The reproduction
A Jupyter notebook or plain Python script that reruns the analysis from the raw file, so a reviewer can check the arithmetic themselves.
§2 Scope
Stating the limits is what makes the rest credible. Forty-two tests are implemented.
Covered
Descriptives and frequencies. Weighted means with verbal interpretation. Correlation. t-tests and their rank equivalents. One-way, two-way, repeated-measures and mixed ANOVA, with post-hocs gated on a significant omnibus. ANCOVA. Chi-square, Fisher’s exact and McNemar. Linear, logistic, ordinal and Poisson regression. Mixed models. Reliability and inter-rater agreement. Exploratory factor analysis. Survival.
Not covered
Structural equation modelling. Confirmatory factor analysis. Item response theory and Rasch. Meta-analysis. Propensity-score matching. Time series. If your design needs one of these, this is not the tool, and the feasibility assessment will say so before you pay.
§3 Rates
The feasibility assessment is free. Credits are spent only when an analysis runs. Across the published studies the estimates run from roughly 25 to 100 credits, and a four-objective survey lands near 50[2].
| Pack | Credits | Price | Per credit | Studies | Action |
|---|---|---|---|---|---|
| Starter | 100 | $5 | $0.050 | ≈ 2 | Choose |
| Researcher | 500 | $20 | $0.040 | ≈ 10 | Choose |
| Lab | 1,500 | $50 | $0.033 | ≈ 30 | Choose |
You see the estimate before you spend, and credits do not expire.
§4 References
The claims above, and where to check them. No account required.
- 1Assumption-driven substitution, worked through end to end. Parametric vs non-parametric: when your data breaks the rules. How normality is checked, why n > 30 is not the licence it is taken for, and what a rank test actually tests.
- 2Twenty complete analyses with their reports, estimates and assumption checks. The sample gallery. Every figure quoted on this page is drawn from them.
- 3The study in Fig. 1. Descriptive-Correlational Survey: Study Habits and Academic Performance. Includes the full report, the assumption diagnostics and the reproduction script.
- 4Free tools, no account: the test chooser, the sample-size calculator, the reliability calculator, the APA formatter, the data cleaner and the chart builder. All six are listed at /tools.
Start with the free assessment. Spend nothing until you agree with the plan.