The hottest Experimental Design Substack posts right now

Social tipping points may not be as impactful as commonly believed
The idea of a committed minority of 25% causing rapid societal change could be overrated
Experimental evidence suggests that the 25% threshold for social tipping points is not highly applicable to real-world social issues

A/B tests are important for businesses because they help test ideas and make informed decisions. Many companies have seen significant revenue increases by using A/B tests.
It's crucial to define the right performance metrics for A/B tests to ensure long-term success. Focus on metrics that show real customer engagement, not just short-term results.
Pay close attention to statistical principles when running A/B tests. Misunderstanding p-values and making hasty conclusions can lead to incorrect results and poor decisions.

The use of machine learning and adaptive experimental design is revolutionizing battery technology for more efficient, reliable, and sustainable energy storage solutions.
Machine learning enhances consumer electronics by optimizing battery life and performance, showing practical benefits in devices like smartphones and electric vehicles.
The combination of machine learning and adaptive experimental design leads to quicker research and innovation in battery technology, making advancements more tailored, responsive, and impactful across industries.

Relying only on randomized experiments can be limiting. It's important to consider all types of evidence based on their quality.
Not every decision needs a complex A/B test; sometimes simpler data or even gut feelings are enough.
We should weigh the cost of getting reliable data against the value it provides. For some choices, high-quality data is a must, but for others, less rigorous information can do the job.

A/B testing compares two treatments to measure impact.
Frequentist A/B testing involves hypothesis formulation, experiment design, and statistical testing.
Bayesian A/B testing incorporates prior beliefs to estimate probabilities directly.

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