Google Ads API v24.1 brings direct A/B experiment stats and new test types

Google Ads API v24.1 adds direct experiment statistics and five new test types, covering AI Max, broad match, video, and Performance Max asset optimization.

NVIDIA (@nvidia) on X

What does it actually take to run A/B testing at nearly a billion-user scale? Prudhvi Vatala, Head of Engineering Platforms at @Snap, explains how his team migrated 10+ petabytes of daily data processing to GPU-accelerated pipelines on @GoogleCloud — cutting job costs by 76% and

Spectry — Find what's hurting conversions and fix it immediately

Heatmaps, session replays, A/B testing, conversion funnels, and AI insights in one privacy-first platform. Replace your scattered analytics stack.

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The Catastrophic Consequences of Agnosticism for Life Searches and a Possible Workaround

Planned and ongoing searches for life, both biological and technological, confront an epistemic barrier concerning false positives - namely, that we don't know what we don't know. The most defensible and agnostic approach is to adopt diffuse (uninformative) priors, not only for the prevalence of life, but also for the prevalence of confounders. We evaluate the resulting Bayes factors between the null and life hypotheses for an idealized experiment with $N_{pos}$ positive labels (biosignature detections) among $N_{tot}$ targets with various priors. Using diffuse priors, the consequences are catastrophic for life detection, requiring at least ${\sim}10^4$ (for some priors ${\sim}10^{13}$) surveyed targets to ever obtain "strong evidence" for life. Accordingly, an HWO-scale survey with $N_{tot}{\sim}25$ would have no prospect of achieving this goal. A previously suggested workaround is to forgo the agnostic confounder prior, by asserting some upper limit on it for example, but we find that the results can be highly sensitive to this choice - as well as difficult to justify. Instead, we suggest a novel solution that retains agnosticism: by dividing the sample into two groups for which the prevalence of life differs, but the confounder rate is global. We show that a $N_{tot}=24$ survey could expect 24% of possible outcomes to produce strong life detections with this strategy, rising to $\geq50$% for $N_{tot}\geq76$. However, AB-testing introduces its own unique challenges to survey design, requiring two groups with differing life prevalence rates (ideally greatly so) but a global confounder rate.

A/B Tests for Beginners - Mediovsky

New to A/B testing? This guide to A/B tests for beginners explains the core mechanism and how to avoid common pitfalls on your first experiment.

Ryan J. Shaw (@RyanJamesShaw) on X

@simonw @TheAmolAvasare And if the latter what does it even mean? If I hit the 2% and don’t get Claude Code on the pricing page but I get Claude Code anyway (“it’s just an A/B test bro”), am I entitled to Code given that it wasn’t included in the offer I accepted? You can’t A/B test a different legal