Today brings both wonderful but also bittersweet news – @OpenAI has announced its intent to acquire @Statsig. @vijayeraji will become the CTO of OpenAI applications, reuniting with @fidjissimo. 1/
We asked @trchan1, Head of Data Science at Statsig, about his upcoming session at #ODSCWest:
“A/B testing has really evolved over the years. In my talk, I’ll dive into advanced techniques that tackle challenges like network effects, peeking,...”
https://t.co/yv3JDRwGBi
Learn common reasons analytics teams fail and how to make your data efforts count in our upcoming virtual webcast 📹
Featuring Head of Data, @trchan1 & Shachar Meir!
Save your spot: https://t.co/9MeN4jGfvA
#datascience#analytics#statsig#abtesting
Ahead of his ODSC West session, Dr. Timothy Chan discusses popular A/B testing methods used in the industry to get ideal results. @statsig https://t.co/GVnr0kkg3c
Ahead of his ODSC West session, Dr. Timothy Chan discusses popular A/B testing methods used in the industry to get ideal results. @statsig@trchan1 https://t.co/JSksXgZJTe
It's officially Day 5 (and final) of Experimentation Week 🖐️
Scaling experimentation is as much a culture challenge as it is a statistical one.
Today we share a suite of collaboration features progressively developed into Statsig's core experimentation workflows!
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Experimentation Week Day 4 - Announcing Interaction Detection 👏
Our customers run 100's of experiments concurrently. We explain how multiple experiments can have an interaction and how you can check for interaction effects on a set of metrics.
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It's Experimentation Week - Day 2 ✌️
We're announcing one-click Stratified Sampling to make your experiment results more trustworthy and consistent.
See how this feature works in Statsig and helps you reduce false positive rates at the same power.
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#datascience#statsig
It's Experimentation Week Day 3 👏
Today, we announce Differential Impact Detection to automatically flag when segments of users respond differently to an experiment - also described as Heterogeneous Effect Detection or Segments of Interest
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Stratified sampling is especially useful for experiments where a tail-end of power users drive a large portion of a metric value, such as in B2B experiments with small sample sizes.
Read more: https://t.co/7GlAR2mIj4
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#abtesting#datascience