Data Contract
Use this section to confirm how Licklider should interpret rows, IDs, outcome structure, and replication before analysis.
Use Data Contract when your question is how Licklider should interpret the structure of your dataset before analysis.
This section is for confirming what each row represents, how IDs and variables should be mapped, what kind of outcome you have, and whether repeated measurements reflect biological or technical replication. It is not the place to choose a statistical method, troubleshoot file upload, or interpret results after analysis.
What this section helps you decide
Use this section when your immediate question is one of these:
- What does each row in my dataset represent?
- Which columns act as subject IDs, group labels, batch IDs, or timepoints?
- Is my outcome continuous, binary, count, proportion, or survival?
- Are repeated rows true biological replicates or technical repeats of the same unit?
These setup decisions affect which downstream checks and analyses make sense, but this category is about declaring the structure first.
Read the page that matches your setup question
| If you need to decide... | Read this page |
|---|---|
| What one row represents in the dataset | Observation Unit Declaration |
| How columns should be interpreted as subject IDs, groups, batches, plates, or timepoints | Variable and ID Mapping |
| What kind of outcome you have and what analysis intent you are declaring | Outcome Type and Analysis Intent |
| Whether repeated measurements are biological replicates or technical replicates | Replicate Structure |
Start here
If you have not yet confirmed what one row means, start with Observation Unit Declaration.
If the row meaning is clear but the column roles are not, continue to Variable and ID Mapping.
If your dataset structure is mostly clear and your next question is about the response variable, go to Outcome Type and Analysis Intent.
If the main risk is repeated measurements from the same animal, patient, well, plate, or sample, go to Replicate Structure.
What this section does not cover
- File format support, table shape, or import failures -> see Data Requirements
- Choosing the statistical method once the data contract is set -> see Methods
- Planning hypothesis direction, sidedness, or exclusion rules -> see Analysis Planning
- Reviewing post-setup warnings about assumptions, independence, or diagnostics -> see Quality Checks
Related
- Related links are registered in frontmatter and rendered below this page.