What This Product Does
Licklider transforms raw experimental data into analysis, figures, and reportable results — with the statistical rigor reviewers expect and a record of every decision that makes it reproducible.
Researchers run experiments. The work that follows — cleaning data, choosing the right test, checking assumptions, building figures, writing methods — takes longer than it should, and leaves more room for error than most people admit.
Licklider transforms raw experimental data into analysis, figures, and reportable results: with the statistical rigor that reviewers expect, and a record of every decision that makes it reproducible.
What Licklider does
You bring tabular data — including raw, pre-processed, or partially cleaned CSVs. Licklider takes it from there.
1. Preprocessing and data quality
Before any analysis runs, Licklider works through the shape of your data: identifying structural issues, flagging outliers with transparent criteria, and recording every transformation made. Nothing happens silently.
2. Analysis and assumption checking
Licklider selects and runs statistical tests appropriate to your data structure and research question. It checks the assumptions those tests require — normality, independence, replication structure — and surfaces problems before they reach a reviewer. When judgment is genuinely yours to make, it says so.
Some errors cannot be inferred from a table alone. If the observation unit, replication hierarchy, pairing structure, or research question is misdeclared or omitted, Licklider may not detect the problem automatically. In those cases, the risk is not just a missed warning: the wrong effective sample size, test family, or interpretation can make p-values and confidence intervals look more convincing than they should.
3. Figures, tables, and methods text
Results don't stop at a p-value. Licklider produces publication-ready figures, statistical summary tables, and methods-section text — not by asking a language model to improvise, but by extracting and organizing the facts from your data and analysis, then expressing them precisely. Depending on the analysis, that includes outputs such as test names, p-values, effect sizes, confidence intervals, assumption flags, figures, and reportable methods text.
What makes Licklider different
Most tools help you execute an analysis. Licklider is built around a different question: can you defend what you did?
Every step — which observations were excluded and why, which test was chosen and on what basis, what the effect size actually was — is recorded, disclosed, and reportable. The output isn't just a result. It's a result you can stand behind.
At the same time, Licklider is not a black box that silently substitutes one method for another. When it makes a recommendation, it shows its reasoning. When the right answer depends on scientific context only you have, it asks rather than decides.
Who it's for
Licklider is built for life sciences researchers working with experimental data.
| Research context | Typical use cases |
|---|---|
| Basic research — cell biology, biochemistry, in vivo studies | Group comparisons, dose-response, repeated measures across animals or conditions |
| Pre-clinical research — assay development, pharmacology, biomarkers | IC50, normality-sensitive analyses, replication structures that reviewers scrutinize |
| Clinical research — observational and interventional studies | Survival analysis, logistic regression, baseline tables, attrition reporting |
If your work ends with a figure or a methods section, Licklider is for you.
What Licklider does not do
Scope boundaries
- It does not validate analyses for regulatory submission or guarantee clinical decision-making
- It does not rewrite your science — research hypotheses, biological interpretation, and scholarly judgment remain yours
- It does not handle image data, real-time data streams, or large-scale computational genomics pipelines
- It does not ghostwrite your paper
- It does not infer every study-design fact from a spreadsheet alone; observation units, hidden nesting, undeclared pairing, and causal interpretation sometimes require researcher input
To understand those limits in more detail, see Observation Unit Declaration and Statistical Independence Check.
Ready to see it in action? Start with the Quickstart →