Bell's Blog

Peeking and early stop is one of the most common mistakes companies do across the board. Learn how sequential testing in the Frequentist approach allows it while maintaining control over false positive rates.

Choosing between one- and two-tailed hypothesis affects every stage of A/B testing. Learn why the hypothesis direction matters and explores the pros and cons of each approach.

Learn why deviations from normality are often not a significant concern in A/B testing, understand the pros and cons of using the t-test in such scenarios, and explore alternative methods to the traditional t-test.

Learn from top expert, Ronny Kohavi, how to build infrastructure required for A/B testing, foster a culture that encourages experimentation, and ensure your teams learn valuable lessons from failures.

Understanding why the uplift seen in A/B tests often differs from real-world outcomes is essential. Explore key reasons behind this discrepancy and insights on how to manage expectations effectively.

CUPED is a powerful technique that not only enhances the sensitivity of your tests but also helps in resource optimization by reducing the number of subjects needed for experiments.

When your advertising efforts are up and sales are increasing, that’s great news, but it doesn't necessarily mean the ads are the reason. Here are three popular tools to measure the lift of a campaign.

Ever wondered why your A/B tests aren’t delivering the results you expected? Reveal the most common A/B testing mistakes made by top companies and how you can avoid them.

Three key trends shaping the future of MMM: the emphasis on causality, the adoption of Bayesian methods, and the push towards real-time analysis.

Robyn, Meridian, Orbit, or custom-made MMM? In this blog post, we compare leading free, paid or custom MMM tools, to help you decide which best fits your needs.

Learn why Bayesian A/B testing can lead to misunderstandings, inflated false positive rates, introduce bias and complicate results rather than clarify them.

In today’s privacy-focused era, the different attribution models create many blind spots for marketing analysts and decision makers. However MMM & Geo Tests can help.

Figuring out an ad's real effect is tricky. Clicks don't tell the whole story and attribution models fall short. The Solution: Geo Testing.

How to plan your marketing spend while measuring the true effect of any marketing activity on your bottom line? The answer lies in Marketing Mix Modeling (MMM).

Traditional methods often fall short in measuring the true impact of marketing strategies. Here's how Geo Tests can resolve common marketing challenges.

In A/B testing, you often have to balance statistical power and how long the test takes. Learn how Allocation, Effect Size, CUPED & Binarization can help you.

Measuring complex user journeys is nearly an impossible task without the proper tools. Learn how MMM & Geo Tests can help advertisers measure the impact of their cross-marketing efforts.