50 things creators say, checked against a real channel every day.

Across the shared pool. For each channel, the share of views over its most recent 28 covered days that arrived from sources the viewer did not choose — Browse, Suggested, Search, hashtags, Shorts, ads — set against the channel's age in days. The complement (direct, external links, playlists, end screens, the channel page, notifications) is where the viewer came back on their own, the same split the public database already uses. Pearson correlation across channels; agrees at r <= -0.4, disagrees at r >= 0.4.

Where the belief comes from: Maturation-creator folklore.

Per video, fit a piecewise function to the impression-by-day curve. Identify the 'elbow' where slope changes most. Agrees if ≥50% of mature videos show a detectable elbow within days 3–14.

Where the belief comes from: Two-phase-algorithm folklore.

Per video, compare impression delivery rate in days 8–14 against days 1–7. Bucket by retention tier (<30%, 30–50%, 50%+). Agrees if 50%+ bucket has impression-rate ratio ≥0.7 AND lower-retention buckets ≤0.4.

Where the belief comes from: TubeBuddy creator guides.

Browse views / Browse impressions broken out by subscribed state. Agrees if subscribed-state share of Browse views ≥50%.

Where the belief comes from: Common assertion; conflicts with documented YouTube guidance.

Across the shared pool, durability read as a half-life. Per channel and per source: take the day that source delivered the most views, fit a line through the log of the next 14 days, and read off how long a halving takes. A source that is not fading over that stretch has no half-life and sits out — reporting a cut-off window as a long half-life would flatter whichever source happened to be climbing. The reading is the pool's median of (Browse half-life over Suggested half-life); agrees at >= 1.5, disagrees at <= 1/1.5. The shared data carries source views per day at the channel level, so this is a channel-level durability read rather than the per-video one the belief describes.

Where the belief comes from: Pickup-pattern folklore.

card_views / total_views. Cards are usually grouped under Direct/Unknown or a specific source.

Where the belief comes from: YouTube official help docs.

Compare AVD, retention pct, sub conversion, comment rate of Channel Page traffic vs Suggested traffic. Agrees if all four are higher for Channel Page.

Where the belief comes from: Common assertion.

Per channel, Pearson correlation between days_since_first_video and trailing-7d Channel Page view share. Agrees if r ≤−0.5.

Where the belief comes from: Library-growth folklore.

For each engagement metric, Pearson correlation with sub conversion rate at the video level. Agrees if comments/v has the highest correlation.

Where the belief comes from: Engagement-depth folklore.

For videos with comparable view counts, regress impression-rate-after-day-7 on (comments per view) and (likes per view) separately. Agrees if |β_comments| > 2 × |β_likes|.

Where the belief comes from: Various creator coaching.

Per video, scale CTR and retention to channel-relative units (each value's distance from the channel's median, divided by the channel's typical spread). Fit a linear regression of total views against both. If the magnitude of the CTR coefficient is at least 1.5× the retention coefficient, the data agrees that CTR dominates on this channel.

Where the belief comes from: MrBeast school of creator advice; widely contested.

Define combined = sqrt(ctr_z × retention_z) per video. Regress total views on combined vs on CTR and retention separately. Agrees if combined- only regression has higher R² than either-alone.

Where the belief comes from: Combined-quality folklore.

Across the shared pool — but this belief is conditional, and the condition is not in the data. It says daily publishers WITH pre-recorded inventory hold their cadence and those WITHOUT it break, so splitting the pool needs the size of each channel's unreleased queue: scheduled and unlisted videos. Only published videos are ever shared, so that split cannot be made. How often daily publishers break cadence IS measurable, but it answers a different question — reporting it here would credit the break to a cause nothing observed. The test names the field it needs and returns a verdict the day that field exists.

Where the belief comes from: Common creator advice.

For each video with ≥14 days tracked, what fraction of total lifetime views came from days 1–3? Channel median across qualifying videos.

Where the belief comes from: VidIQ and TubeBuddy guides; widely repeated in creator literature.

For videos ≥60 days old, coefficient of variation of daily views in days 30–60. Channel median. Agrees if median CoV ≤0.5.

Where the belief comes from: Common analytics-tool framing.

Same as #12, but agrees if post-change views stay ≤50% of pre-change baseline for 14+ days. Tests for sustained damage rather than reset+recovery.

Where the belief comes from: Conventional wisdom (the 'no-fiddle' rule).

Per video, end-screen view share of total views vs lifetime weighted retention. Agrees if r ≥0.4. Requires Round 4 cards/end-screen data.

Where the belief comes from: End-screen optimization folklore.

end_screen_views / total_views channel-wide. Reads from per-source breakdown. Note: requires retention curve / cards data from Round 4 to test fully.

Where the belief comes from: YouTube official help docs (creator-side guidance).

AVD, retention, sub conversion of External vs all other sources. Agrees if External AVD ≥ p75 of cross-source AVD distribution. Sample-size flag triggers when External views < 100 channel-wide.

Where the belief comes from: Common assertion.

Days from 1 sub to 100 subs vs days from 100 to 200, 200 to 300, etc. Channel-relative growth rates per sub-bucket. Agrees if days-per-sub in 0–100 is ≥2× days-per-sub in 100–500.

Where the belief comes from: Beginner-creator coaching.

Across the shared pool. The belief never fixed a definition of "predicts", so this one is stated plainly: the TRAJECTORY is the slope of first-week views across a channel's first ten videos (rising, flat or falling), and the month-12 STATE is that channel's total views one year after its first upload. Pearson correlation across channels; agrees at r >= 0.5, disagrees at r <= -0.5. A channel needs ten videos with a full first week and a shared day on or after its own first anniversary; those short of either are counted in the evidence rather than filled in.

Where the belief comes from: Long-arc folklore.

Pearson correlation of day1_views vs total_views across all videos with ≥30 days tracked. Same correlation for day1_ctr vs lifetime_weighted_ctr. Higher r = belief agrees on this channel.

Where the belief comes from: Closely related to #2; cited as 'velocity matters'.

Across the shared pool. For each channel: the views its FIRST video had banked by day 30, against that channel's own total views over its first 90 days. Pearson correlation across channels; agrees at r >= 0.5, disagrees at r <= -0.5. Both windows must lie wholly inside a channel's shared coverage — a channel that shared only part of either window sits the round out rather than contributing an understated total.

Where the belief comes from: TenfoldGrowth-style coaching.

For each video pair (N−1, N) where N−1 is still in days 1–7 of its lifecycle when N publishes, compare N−1's view trajectory in the 3 days after N's publish to N−1's trajectory in the 3 days before. Channel median of (views_3d_after / views_3d_before) across eligible pairs.

Where the belief comes from: Common creator advice; cited across creator coaching channels.

From video retention curve, retention_15s_pct. Pearson correlation with total views and weighted_ctr_post_day_3. Agrees if r ≥0.7. Requires retention curve data (Round 4).

Where the belief comes from: Hook-economy folklore.

Requires hourly data not exposed by the YouTube Reporting API.

Where the belief comes from: Velocity-creator folklore.

Pearson correlation of duration_seconds and (total_watch_minutes / total_views). Agrees if r ≥0.5.

Where the belief comes from: Logical inference (bounded by retention).

Bucket videos by duration (60/180/360/600/1200/1800/3600+ sec). Median weighted CTR, AVD, retention pct, sub conversion per bucket. Agrees if 600s bucket has the highest median weighted retention pct.

Where the belief comes from: Creator-economy folklore; pre-2018 mid-roll-ad threshold.

When video N publishes, change in non-N videos' impression share over the next 3 days. Agrees if median impression-share-of-others drops ≥15% in the post-publish window.

Where the belief comes from: Catalog-cannibalization folklore.

Across the shared pool — but "narrow focus" has to be measured across a channel's OWN videos, which needs per-video topic signal (titles are the usual stand-in). The shared data carries one category per channel and no per-video text, and a single label cannot describe variation inside the channel it labels: two channels in one category may be a specialist and a generalist. Grading by category and calling it focus would be an invented measurement. The test names the field it needs; no number of extra channels supplies it.

Where the belief comes from: Niche-down folklore.

Median weighted CTR and median total views for title_has_number = 1 vs 0. Agrees if both metrics ≥1.2× the non-numbered group.

Where the belief comes from: Listicle-era folklore.

Median day-1 views by hour-of-publish. Identify mode. Agrees if mode falls in 14:00–18:00 local time. Many channels lock to one hour, making this inconclusive.

Where the belief comes from: Publishing-time folklore.

Cadence regularity score = the typical day-to-day spread of inter-publish gaps, divided by the mean gap (coefficient of variation). Lower = more consistent. Correlate against trailing-30-day view growth.

Where the belief comes from: Universal creator-coaching advice.

Same calculation as #1.

Where the belief comes from: Aliased to #1 frequency_cannibalization (different phrasing of same belief).

For gaps of ≥5 days between publishes, compare the post-gap video's day-1/day-3 performance against the channel's typical day-1/day-3.

Where the belief comes from: Common creator coaching.

Median weighted CTR for videos where title_is_question = 1 vs 0. Owner-DB only — not in the public DB (titles are coarsened in public).

Where the belief comes from: Title-optimization folklore.

Coefficient of variation of weekly weighted CTR and AVD across the channel's history. Compare CoV in weeks 1–10 vs weeks 11+. Agrees if both CoVs drop by ≥30% after the 30-video mark.

Where the belief comes from: Maturation folklore.

Same regression as #7; agrees if |β_retention| > 1.5 × |β_ctr|. Note: can be `inconclusive` simultaneously with #7 if neither dominates.

Where the belief comes from: Sean Cannell / Think Media school; opposite of #7.

Per video, slope of weekly Search-source view share over the video's lifetime. Channel-level: share of mature videos with positive slope.

Where the belief comes from: SEO-creator advice.

Pearson correlation of duration_seconds and total_views. Agrees if r ≤−0.3.

Where the belief comes from: Pre-Shorts-era folklore.

Gini coefficient on subs_gained distribution across videos. Agrees if Gini ≥0.7.

Where the belief comes from: Power-law folklore.

Across the shared pool. Subscribers per view (lifetime subscribers over lifetime views) against how far into a video the audience typically watches — the median halfway-mark watch ratio across that channel's videos. Pearson correlation across channels; agrees at r >= 0.3, disagrees at r <= -0.3. The belief names three engagement reads; the shared data carries retention only, and the evidence records comment rate and average view duration as absent rather than substituting something else for them.

Where the belief comes from: Engagement-quality folklore.

AVD per (video, subscribed_state). Agrees if subscribed AVD median is ≥1.3× non-subscribed AVD.

Where the belief comes from: Common assertion.

Per-video, what % of day-0 views came from subscribed-state viewers? Channel median. Reads from summary_video_subscribed (owner-DB only — not in the public DB).

Where the belief comes from: Common assertion in creator coaching.

Suggested views per subscribed state. Agrees if non-subscribed share of Suggested views ≥70%.

Where the belief comes from: Common assertion.

Across the shared pool. For each channel that crossed 1,000 subscribers inside its own shared coverage: mean daily views over the 30 days AFTER the crossing, divided by the mean over the 30 days before it. The reading is the pool's median of those ratios; agrees at >= 1.5, disagrees at <= 1/1.5. Both windows must lie inside coverage, so a channel already past 1,000 on its first shared day has no crossing to read.

Where the belief comes from: Monetization-threshold folklore.

For videos with a title change, compare daily views/impressions/CTR for the 7 days before vs 7 days after the change. Agrees if median ratio drops below 0.5 then recovers above 1.0 within 14 days.

Where the belief comes from: Common YouTube growth-channel advice.

Distribution of peak_views_day across mature videos. Agrees if ≥60% of videos peaked on days 0–2.

Where the belief comes from: Pre-2020 algorithmic-period folklore.

Across the shared pool, on a stand-in: a session spans videos and often channels, and nothing shared here can see one. The stand-in is the minutes a typical view spends watching — each video's length times how far its audience watched, weighted by first-week views — set against the channel's recent view growth (its last 28 covered days over the 28 before them). Pearson correlation across channels; agrees at r >= 0.3, disagrees at r <= -0.3. Confidence stays low at every pool size: this stands in for session length, it does not measure it.

Where the belief comes from: Session-watch folklore (post-2018 algorithm framing).

Median weighted CTR, AVD, sub conversion for videos published on Sat/Sun vs Mon–Fri. Agrees if all three are lower on weekend uploads. Inconclusive with <10 weekend uploads.

Where the belief comes from: Day-of-week folklore.