Reading your results

A results page puts the computed numbers first and the explanations after them. Everything above the AI interpretation is computed by Celeus’s engine by fixed rules, so the same run always shows the same numbers and the same wording. This page explains how to read each part.

The headline result

The headline line gives:

• n - the number of observations the analysis used, after any missing-data handling and population filter.
• The estimate - the size and direction of the effect the test measures, for example a difference in means, an odds ratio or a hazard ratio. Its label says which.
• The 95% confidence interval - the range of effect sizes compatible with your data at the 95% level. A narrow interval means a precise estimate; a wide one means the data pin the effect down only loosely.
• The p-value - how surprising data like yours would be if there were truly no effect. A small p-value is evidence against “no effect”. It is not the probability that the effect is real, and it says nothing about how large or important the effect is.
• The effect size - a standardized measure of magnitude, such as Cohen’s d, which lets you judge whether an effect is large enough to matter, independent of sample size.
• The seed - the fixed random seed the run used, so it can be repeated exactly.

Read the estimate and its interval first, then the p-value. A result can be statistically significant and too small to matter, or not significant and still compatible with an important effect when the interval is wide.

A descriptive analysis, such as a Table 1, has no p-value and says so.

Assumption checks

Every test relies on assumptions, such as normally distributed residuals, equal variances or proportional hazards. Celeus checks the ones that matter for the chosen test against your data and marks each as passed or to check.

A failed check does not invalidate a result by itself. It is a signal to look at the suggested alternative test, which the flags and the suitability panel point to.

Warning flags

Flags are rule-based cautions raised from the run itself: a failed assumption, low statistical power, many rows dropped for missing data, a p-value close to the threshold. They are computed by the engine, not the AI, and they travel with the reproducible package, so a reviewer sees what you see.

Some flags come with a one-click follow-up, such as re-running with a different test or a different missing-data strategy. Every run stays in the record. Choose the test from the assumptions, not from the p-value you prefer.

When a non-significant result comes from a small study, a power flag says what size of effect the study could reliably have detected. That distinguishes “no effect” from “too little data to tell”.

Suitability and the method decision

The suitability panel grades the chosen test and its alternatives from the assumption checks on your data: Recommended, Suitable, Use with caution or Not appropriate. The same data always get the same grade.

The method decision panel records how your choice compares with what Celeus’s advisor would have ranked first for this data. Choosing a suitable method that was not ranked first is a normal decision. If the advisor would not have suggested the method at all, the panel says so, and you should be ready to explain why it fits your design.

What this means

The what this means card gives plain-language sentences computed from the results by fixed rules. They describe associations in this dataset, not causes, and they are not advice about any individual patient.

The AI interpretation

If you asked for one, the interpretation is written by the AI from the computed results and is marked as AI-written. It builds on the numbers above and never replaces them: if the two ever disagree, the numbers are right. Use it as a reading aid, and read the flags and assumption checks yourself.

Before you confirm

The verify before accepting banner asks you to check what was analyzed, the numbers and the flags. Confirming records on the run who reviewed it and when, and unlocks the package download. See Export results and the reproducible package.

Celeus analyzes research data. Its results describe your dataset; they are not a basis for diagnosing or treating an individual patient.