<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>dackohn blog</title><link>https://dackohn.com/blog/</link><description>Notes on building practical AI systems and statistics tools.</description><item><title>Building an Offline AI That Turns Meetings Into Minutes: Six Lessons</title><link>https://dackohn.com/blog/offline-meeting-minutes-ai.html</link><guid>https://dackohn.com/blog/offline-meeting-minutes-ai.html</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Six lessons from building a fully offline pipeline that turns meeting audio into structured minutes: multilingual speech recognition, hallucination checks, structured extraction with a local LLM, and why practical AI is mostly engineering.</description></item><item><title>Central Limit Theorem Simulation: Watch It Work (and Fail) With Interactive Demos</title><link>https://dackohn.com/blog/central-limit-theorem-simulation.html</link><guid>https://dackohn.com/blog/central-limit-theorem-simulation.html</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Watch skewed data, dice and two-humped populations turn into bell curves, see how big n really needs to be, and find out when the Central Limit Theorem fails.</description></item><item><title>What Makes a Free Online Statistics Tool Reliable and Accurate?</title><link>https://dackohn.com/blog/what-makes-statistics-tools-reliable.html</link><guid>https://dackohn.com/blog/what-makes-statistics-tools-reliable.html</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>How to tell whether a free statistics calculator is right: method choice, checks against reference software, simulation, edge cases, transparency, and the real problems our own checks caught.</description></item><item><title>Interpreting Effect Size for Better Data Storytelling</title><link>https://dackohn.com/blog/interpreting-effect-size.html</link><guid>https://dackohn.com/blog/interpreting-effect-size.html</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Turn effect sizes into a story readers understand: Cohen's d in plain language, why the small/medium/large labels mislead, and how to report effects with uncertainty. Includes an interactive visualizer.</description></item><item><title>How to Compare Groups With Chi-Square and Nonparametric Tests</title><link>https://dackohn.com/blog/compare-groups-chi-square-nonparametric.html</link><guid>https://dackohn.com/blog/compare-groups-chi-square-nonparametric.html</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Chi-square and Fisher's exact test for categories, Mann-Whitney, Wilcoxon and Kruskal-Wallis for ordinal or non-normal data: which to use, worked examples, and effect sizes.</description></item><item><title>How to Document Statistical Analyses in a Report or Thesis</title><link>https://dackohn.com/blog/document-statistical-analysis-thesis.html</link><guid>https://dackohn.com/blog/document-statistical-analysis-thesis.html</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>A checklist for writing up statistical analyses in a thesis or report, with APA-style reporting examples for t-tests, ANOVA, chi-square, correlation and Mann-Whitney, and the mistakes to avoid.</description></item></channel></rss>
