Teaching · For Teachers

Check the assumptions before you trust the test.

Pick the analysis and mark each assumption. The summary gives the usual alternative for anything violated and computes the spread ratio from your groups.

Check an analysis What you get

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Included with Caveat Classroom and Whole-school licenses. Or get it in the For Teachers toolkit, $79 for 72 items.

The analysis

One record per planned analysis. The example is made up.

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What you get

Teaching · For Teachers$12 · included with Classroom and Whole-school licenses

What you finish with: Which assumptions hold, the usual alternative for each one that does not, and what to check next.
Who it is for: Teacher-researchers, graduate students in education and school data staff.
Format: Browser checklist. One saved record per analysis; printed for your methods notes or as a blank form; CSV; export and import a file.
Access: $12 one-time, yours on every device you sign in on; the tool opens once you are signed in with it. Included with Caveat Classroom and Whole-school licenses. Also in the For Teachers toolkit ($79) and the Teaching Suite pass ($199), and 1 more bundle. Everything you type stays in this browser; nothing is sent to Caveat.
Where it lives: The For Teachers space in Caveat Teaching.
Support: Questions about an order, access or a correction: team@caveatacademic.com.

Drawn for this page. Every test rests on assumptions; checking them first tells you which test your data can support.

How to use it

Start a record with a short study code.
Choose the analysis you plan to run.
Check each assumption with a plot or a count, then mark it.
Enter each group’s size and standard deviation.
Report the checks you ran with your results.

Check an analysis

How to read the checks

Rules of thumb

Textbooks differ. A largest-to-smallest standard deviation ratio over 2 is a common warning sign for unequal variances, and the chi-square test is usually trusted when no expected count is below 1 and at least 80 percent are 5 or more (Cochran’s guideline).

Alternatives

Welch’s t test does not assume equal variances and is a sound default for two groups. Rank-based tests suit skewed data with small groups. Students in the same class are not independent; a multilevel model or class-level analysis accounts for that.

What it is not

An organizer for your checks. It does not run the analysis or test the assumptions for you; use your statistics software for the plots and tests.

Sources

Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE: chapters on assumptions.
Delacre, M., Lakens, D., & Leys, C. (2017). Why psychologists should by default use Welch’s t-test instead of Student’s t-test. International Review of Social Psychology, 30(1), 92–101.
Cochran, W. G. (1954). Some methods for strengthening the common χ² tests. Biometrics, 10(4), 417–451.
What Works Clearinghouse. Procedures and Standards Handbook, Version 5.0 (2022): clustering and analysis.

The example analysis is made up. Support: team@caveatacademic.com.