Channel
@abtesting
On this record: Growth · Engagement · Posts · Citations · Cite this entry
5,680subscribers
-8 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001467950897 |
|---|---|
| Type | Channel |
| Username | @abtesting |
| Created | 3 May 2019 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/abtesting |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 18:26 | 5,680 | -4 |
| 9 Aug 2026, 13:01 | 5,684 | -4 |
| 6 Aug 2026, 10:32 | 5,688 | no change |
| 6 Aug 2026, 09:46 | 5,688 | first reading |
20 posts held, back to 8 May 2023 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 8 pagesof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 1 May 2026. An engagement rate over an empty window would be a number about nothing.
Наткнувся на цікаву ідею про privacy-by-design в A/B testing. Суть дуже проста: щоб аналізувати результати експериментів, не обов’язково зберігати дані по кожному окремому користувачу. У багатьох випадках достатньо працювати з агрегованими даними — тобто не бачити конкретну людину, а одразу дивитись на групи користувачів. Для великого і відповідального бізнесу це може бути хорошим підходом одразу з кількох причин…
Цікавий та інтерактивний спосіб краще зрозуміти перевірку гіпотез за допомогою ігрового симулятора A/B-тестування від Лукаса Вермеєра. @ABtesting
Гарне порівняння підходів до аналізу експериментів Автор аналізує обмеження традиційного A/B-тестування та пропонує альтернативний підхід на основі баєсівської статистики. Баєсівський підхід пропонує більш гнучкий та надійний спосіб оцінки ефективності змін у продукті чи маркетингових кампаніях. Використання баєсівських методів може допомогти швидше приймати обґрунтованіші бізнес-рішення. via @ABtesting
The unusual A/B Test, with Heterogenous Treatment Effects The article explores the limitations of traditional A/B testing when treatment effects are heterogeneous—that is, when changes don’t affect all users or segments equally. In search engines, changes to algorithms might significantly impact some queries via @ABtesting
Подавай заявку на навчання з аналітики від Genesis Academy! Обирай програму, яка найкраще відповідає твоєму запиту: 📊 Genesis Analytics Week — відкритий тиждень live-лекцій і воркшопів, що допоможе розібратися з основами аналітики в IT-бізнесі. Без відбору — участь за реєстрацією. Після навчання ти: ▪️знатимеш, які бувають ролі аналітиків у продуктовому ІТ; ▪️розумітимеш метрики та їхню взаємозалежність; ▪️отрим…
False Positive Risk in A/B Testing There is a much greater probability than generally expected that a statistically significant test outcome is in fact a false positive. In industry jargon: that a variant has been identified as a “winner” when it is not. https://georgi-georgiev.medium.com/false-positive-risk-in-a-b-testing-ba2c76e258c4 via @ABtesting
The good Lukas Vermeer’s A/B testing game simulator is here. It’s an interactive way to understand hypothesis testing better. You control the backlog and running experiments for a small development team. Each day you make decisions that impact product development and overall sales. Your objective is to maximise total sales. via @ABtesting
It takes a Flywheel to Fly: Kickstarting and Keeping the A/B testing Momentum Practitioners from hundreds of companies and several different industries reached out with thoughts, concerns and tips on how to kickstart and scale an A/B testing program from “Crawl” or “Walk” milestones, to the “Run” or “Fly” stages. via @ABtesting
Four Ways to Improve Statistical Power in A/B Testing (without increasing test duration, duh) Exploration simple and potent techniques for enhancing power without prolonging test durations. By grasping the significance of key parameters such as allocation, MDE, and chosen KPIs, data analysts can implement straightforward strategies to elevate the effectiveness of their testing endeavors. This, in turn, enables inc…
Beyond the limitation of A/B Testing using Causal Inference In the realm of product management and development, understanding the impact of new campaign (aka treatment) releases on user behavior is crucial. Campaign assessing effect on key performance indicators, such as retention metrics (specifically, Day 1 retention or D1), becomes a pivotal task. However, this task presents several challenges. While A/B testi…
Ми в MacPaw шукаємо продуктового аналітика з досвідом у web-аналітиці та бажанням працювати над маркетинговими задачами. MacPaw - продуктова ІТ компанія, розробляє macOS/iOS-додатки, продукти якої встановлені на кожному 5-му макбуці світу. 40+ аналітиків, багато різноманітних сервісних команд, які допомагають продуктовим аналітикам робити їх роботу краще. Можливість працювати віддалено. Вакансія за посиланням: …
Computing Minimum Sample Size for A/B Tests in Statsmodels: How and Why A deep-dive into how and why Statsmodels uses numerical optimization instead of closed-form formulas. @ABtesting
Showing the 12 most recent of 20 posts we hold for @abtesting. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank — 634,140 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 12 August 2026 — this entry's latest reading, not the date you are reading this.
“A/B testing” (@abtesting), 5,680 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/abtesting.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.