OpenScience can help inspect a dataset, implement statistical methods in Python or R, and explain the results. Begin with the study design so the analysis answers the intended question.
Describe the observation and comparison
Provide the outcome, units, treatment or predictor, grouping variables, and repeated measurements. Explain how samples were collected and whether the analysis was planned before observing results.
Plan an analysis of data/assay.csv. Each biological sample has three
technical repeats, and samples were measured across two batches. Compare
the treatment groups without treating technical repeats as independent
biological samples. Ask about missing design details before fitting.
Inspect before modeling
Ask for missingness, duplicate identifiers, distributions, extreme values, and group sizes. Preserve the original data and record proposed exclusions separately.
Use Tables for interactive inspection and Python and R for full-file calculations.
Choose and explain a method
Ask OpenScience to explain the estimand, model or test, assumptions, covariates, and validation strategy. If several methods are plausible, compare their assumptions before choosing one.
Propose the primary analysis and one sensitivity analysis. Explain which
assumptions can be checked, which depend on study design, and what the
method cannot establish. Do not choose a method only because it gives a
smaller p-value.
Request interpretable outputs
| Output | Why it matters |
|---|---|
| Sample and exclusion counts | Shows which observations support the result. |
| Effect estimate and units | States the size and direction of the comparison. |
| Interval estimate | Communicates uncertainty under the chosen method. |
| Diagnostics | Exposes model fit or assumption problems. |
| Sensitivity analysis | Shows dependence on defensible analytical choices. |
| Reproducible script | Makes transformations and calculations inspectable. |
For multiple comparisons, ask how multiplicity was handled. For observational data, keep association and causal interpretation separate.
Run and package the analysis
Implement the agreed analysis, set seeds where randomness is used, and
save the script, effect table, diagnostic plots, and a methods note.
Rerun from the original inputs and report whether the result matches.
The output should state unresolved assumptions and data limitations alongside the result. Statistical software completing successfully is not evidence that the method fits the study design.
Continue with Figures and Writing to communicate the result.