When and why to run tests?
Testing is not unique to software development. It is an essential part of research. Most researchers already test their work informally: they interrogate raw data for issues, scan tables, inspect plots, and check that values fall within plausible ranges.
Formal testing simply records those expectations in code so they can be run consistently over time, catching issues as they arise rather than months later. Tests ask questions like:
Are any values impossible or out of range? (Is a proportion between 0 and 1? Are dates in the expected window? Are indicators within tolerance?)
Does the code still run after I change something? Can the analysis run end to end without error?
This is the same logic you apply when checking results manually, just automated so you can repeat it systematically.
Why tests matter in research projects
Writing tests pays off because research code and data evolve. When you change your code, introduce new features, or update your data, you risk breaking things that used to work. Tests catch these problems early.
They help you verify that:
Your logic and processes are working as you believe they are.
Recent changes don’t break existing code.
Updated datasets are still valid and free from unexpected anomalies.
Your results remain consistent and reproducible.
Over time, when multiple people work on a project, or when you revisit analyses months later, tests become invaluable. They ensure that re-running your work produces the same results as it did previously, and that anyone depending on your outputs can trust them.
Ultimately, tests are not about best practice. They are about verifying your analysis, so you can trust that your results mean what you think they mean.
When to write tests
You don’t need a finished analysis to start testing. Even a single function or data processing step is worth testing. As you build your analysis, follow a simple pattern:
Write a piece of code (a function, data processing step, calculation).
Inspect the output to check it looks right.
Turn that check into a test so you can run it automatically.
If you spot a bug, write a test that catches it.
Fix the bug, knowing your test will catch it if it happens again.
This way, testing becomes part of your natural workflow rather than a separate task.
Avoiding leaving all testing until the end of your analysis. Problems discovered late are much harder to fix, and you may not fully understand their impact on your results. Testing as you go makes the process feel natural and keeps issues manageable.
Some software engineers write tests before code - this is called “test-driven development”. This can feel less relevant in exploratory academic research, but if it suits your workflow, it is worth trying.
When to run tests
Run tests regularly after any code or data changes, as catching errors earlier makes them easier to fix.
This practice of re-running tests is called regression testing, and it ensures recent changes haven’t introduced errors.