Evidence
Vedic Horā tests its own rules against documented history and publishes what came back — including the tests that found nothing. An accuracy page that only reported hits would not be evidence; it would be advertising.
How the natal test works
A dataset of public figures with timed births, each carrying a Rodden rating for the reliability of that birth time, and each with individually sourced, dated life events. Horā scores every chart on each life domain without seeing the events, then asks two questions.
- Promise. Do charts Horā scores highly in a domain actually show more favourable events in that domain than charts it scores low?
- Timing. Do dated events fall inside the daśā windows Horā flagged, more often than chance would put them there?
The chance baseline is not assumed — it is computed from the actual span of the flagged windows against the scanned lifetime, and only the top windows per domain are kept, which makes the containment test deliberately strict.
Natal results
- marriage: score bands do not clearly separate outcomes on this sample (n=79) — no claim made.
- business: favourable-event share rises monotonically with the chart's business score band (40% → 73% → 83%, n=28) — directionally consistent with the classical promise, on a small biased sample.
- marriage timing: 0% of dated events fell inside the top favourable daśā windows vs 4% expected by chance (n=79) — below chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
- health timing: 4% of dated events fell inside the top favourable daśā windows vs 3% expected by chance (n=52) — above chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
- career timing: 7% of dated events fell inside the top favourable daśā windows vs 5% expected by chance (n=77) — near chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
- business timing: 7% of dated events fell inside the top favourable daśā windows vs 4% expected by chance (n=28) — above chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
- children timing: 8% of dated events fell inside the top favourable daśā windows vs 5% expected by chance (n=36) — above chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
- foreign timing: 0% of dated events fell inside the top favourable daśā windows vs 7% expected by chance (n=19) — below chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
- wealth timing: 6% of dated events fell inside the top favourable daśā windows vs 6% expected by chance (n=18) — near chance. Caveats: only the top-6 windows per domain are kept (a very strict containment test) and year-only events are tested at mid-year.
Observed frequencies among documented public-figure cases (Rodden-rated timed births; every event sourced). Small, selection-biased sample — famous documented lives, not the general population. These are historical correlations shown as evidence, NOT personal probabilities, and are reported separately from the rule-based reading.
Ruleset [object Object] · full machine-readable report at /api/empirical-report.
Mundane astrology
A separate catalogue of dated world events — earthquakes with recorded times, political ruptures, market shocks — tested against classical mundane indicators from the Bṛhat Saṁhitā tradition, each compared with a chance baseline built by sampling every day across more than a century.
Computed on demand — it takes a few seconds the first time.
How to read all of this
These are observed frequencies among famous, well-documented lives. That is a small and badly biased sample — history records the dramatic and forgets the ordinary. The numbers are historical correlations offered as evidence about the rules; they are not probabilities about you, and Horā never blends them into a reading’s confidence score. They are shown alongside it, separately, on purpose.
Some results came out directionally consistent with the classical claim. Several came out flat, and those say so in as many words. Both kinds are on this page, and both will stay here as the dataset grows.
Now check it against your own chart.
Every statement carries its source. Follow one into the text.
Cast your chart