result-interpretation

Name: result-interpretation
Availability: InStock
Author: openscientist-io

Interpret statistical results and generate follow-up actions or hypotheses.

44|13|Updated Nov 15, 2025

Search Tags:#statistics #interpretation #hypotheses #effect-size #p-value #literature-search #knowledge-state

Ready to run

npx skills add https://github.com/openscientist-io/openscientist --skill result-interpretation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: result-interpretation
Source: https://github.com/openscientist-io/openscientist/tree/main/skills/workflow/result-interpretation
Command: npx skills add https://github.com/openscientist-io/openscientist --skill result-interpretation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps researchers and data scientists transform raw statistical outputs into actionable insights by standardizing interpretation, documentation, and follow-up steps.

Core Features & Use Cases

Interpret diverse result types (positive, negative, borderline, unexpected) and translate into next steps.
Update knowledge state with interpretations and evidence.
Generate follow-up hypotheses and plan targeted experiments or literature checks.
Use Case: A user has p-values and effect sizes from multiple tests and wants a consistent interpretation workflow.

Quick Start

Interpret the latest statistical results and receive recommended interpretations and next-step suggestions.

Frequently Asked Questions about result-interpretation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I interpret p-values and effect sizes from multiple statistical tests consistently?▼

Standardize statistical result interpretation by inputting structured p-value and effect size data to automate decision-making guidance for subsequent steps. This provides a consistent workflow to evaluate positive, negative, borderline, and unexpected outcomes across research domains.

Can I automate follow-up hypothesis generation after analyzing experimental results?▼

Yes, automating follow-up hypothesis generation translates interpreted statistical outcomes into actionable next steps. It updates your knowledge state with evidence and generates targeted follow-up hypotheses or experiments with supporting documentation.

Does statistical result interpretation work for psychology and biology research domains?▼

Result interpretation applies across biology, psychology, and other research domains. It processes structured statistical outputs like confidence intervals and effect sizes, guiding domain-agnostic decisions on recording findings or searching literature.

What is the best way to decide whether to search literature or record findings after data analysis?▼

Automated interpretation of statistical results recommends whether to record findings, search literature, or generate new hypotheses. It standardizes this decision-making by evaluating structured result data and updating your knowledge state with the evidence.

How do I update my knowledge state with new statistical evidence and interpretations?▼

Update a knowledge state by feeding structured statistical results into the interpretation workflow. The process evaluates p-values and effect sizes, records the interpretations as evidence, and outputs follow-up actions with supporting documentation.

What limitations exist when automating the interpretation of borderline or unexpected statistical results?▼

Automated interpretation of borderline or unexpected statistical results requires structured result data input to function. Without properly formatted p-values, effect sizes, and confidence intervals, generating accurate follow-up hypotheses and decision guidance is not possible.