# Standard Error
> AI products are increasingly described through benchmark scores, survey findings, accuracy percentages and claims about human behavior.

## Entries
- [How to Read an AI Benchmark Table: What the Footnotes Leave Out](https://standard-error.pages.dev/reading-ai-benchmark-footnotes/): An AI benchmark table reports performance on a defined evaluation, not general intelligence or guaranteed performance in everyday use.
- [How to Read a Workplace AI Survey: Seven Questions About the Sample](https://standard-error.pages.dev/auditing-ai-workplace-survey-samples/): A survey reporting that many people use AI at work is informative only when readers know who was asked, how they were recruited and what counted as use.
- [How to Interpret Claims About AI Chat Frequency and Wellbeing](https://standard-error.pages.dev/interpreting-ai-chat-wellbeing-claims/): A claim that frequent AI chat is associated with better or worse wellbeing describes a relationship between measured variables, not necessarily an effect.
- [Confidence Intervals Without the Jargon: A Practical Guide With Worked Examples](https://standard-error.pages.dev/confidence-intervals-practical-guide/): A confidence interval describes the uncertainty around an estimate under a particular statistical procedure.
- [Preregistration for AI Product Comparisons: A Practical Template for Small Newsrooms](https://standard-error.pages.dev/preregistration-product-comparisons-newsrooms/): Preregistration records the questions, procedures, and analysis decisions for an AI product comparison before the results are examined.
- [How to Read an AI Companion Feature Page: Claims, Evidence, and Open Questions](https://standard-error.pages.dev/ai-companion-feature-page-reading-guide/): An AI companion feature page describes a product's intended capabilities and presentation, but its statements do not all carry the same evidential weight.

## About
- [About Standard Error](https://standard-error.pages.dev/about/): what the site covers and how entries are made

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