I run Monte Carlo simulations of content production over random Watts-Strogatz graphs to show various effects relevant to modeling and understanding Randomized Controlled Trials on social networks: the network effect, spillover effect, experiment dampening effect, intrinsic dampening effect, clustering effect, degree distribution effect and the experiment size effect. I will also define some simple metrics to measure their strength. When running experiments these potentially unexpected effects must be understood and controlled for in some manner, such as modeling the underlying graph structure to establish a baseline.

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The article illustrates how Monte Carlo simulations serve as a powerful method for exploring statistical concepts in A/B testing, enhancing understanding, improving experimental design and analysis.

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Recently, I delivered a techtalk on A/B testing to an audience of non-technical Product Managers and experienced Data Scientists.

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Why are Randomized Controlled Trials (RCTs, known as A/B testing in much of the industry) testing is widely regarded as the golden standard of causal inference? What else can a Data Scientist do if A/B testing is not possible, and why are those alternatives inferior to A/B testing?

This papers shows, using 15 experiments (for ads on Facebook) where a RCT was conducted, that common observational methods (run on the Facebook data, by ignoring the control group) severely mis-estimate the true treatment life (as measured by the RCT), often by a factor of 3x or more. This is true, even though Facebook has (i) very large sample sizes, and, (ii) very high quality data (per-user feature vector) about its users which are used in the observational methods. This should be a major red flag for Data Scientists working on common marketing measurements (such as marketing campaigns) using observational methods.

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I run Monte Carlo simulations of content production over random Watts-Strogatz graphs to show various effects relevant to modeling and understanding Randomized Controlled Trials on social networks: the network effect, spillover effect, experiment dampening effect, intrinsic dampening effect, clustering effect, degree distribution effect and the experiment size effect. I will also define some simple metrics to measure their strength. When running experiments these potentially unexpected effects must be understood and controlled for in some manner, such as modeling the underlying graph structure to establish a baseline.

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I discuss five lessons from large-scale experiments conducted by Google, Bing, Netflix and Alibaba: Kohavi's 1 out of 3 rule, Google's 41 shades of blue, Bing's unexpected big win, Alibaba's personalization experiment and Netflix' movie image personalization.

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I use toy Monte Carlo simulations to demonstrate 5 ways to reduce variance in A/B testing: increase sample size, move towards a more even split, reduce variance in the metric definition, stratification and CUPED.

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In this final blog post about CUPED, I will address some questions about CUPED, such as, is correlation between "before" and "after" the same as seasonality?

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I use Monte Carlo simulations of A/A tests to demonstrate how Data Scientists can incorrectly skew lift and p-values if they pick-and-choose between reporting traditional and CUPED results after the experiment has concluded.

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I use Monte Carlo simulations of conversion A/B tests to demonstrate how CUPED reduces measurement variance in conversion experiments.

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I use Monte Carlo simulations of A/B tests to demonstrate CUPED, a method to use historic "before" data to reduce the variance in the measurement of the treatment lift.

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This is the transcript of a talk I did on experimentation and A/B testing to give the audience an intuitive understanding of p-values and statistical significance.

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After the 2020 US presidential election, the Trump campaign filed over 50 lawsuits and attacked the integrity of the elections by claiming there was voter fraud. One of the last lawsuits was filed in the Supreme Court of the United States by the state of Texas. Here I look at the statistical claims made in this lawsuit that were supposed to show irregularities in the Georgia vote.

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In real-life, non-digital situations, it's often not feasible to run true A/B tests. In such cases, we can compare before and after rollout conversions at a treatment site, while using a similar control site to measure and correct for seasonality. The post discusses how to compute increasingly correct p-values and bayesian probabilities in such scenarios.

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I use Monte Carlo simulations to explore the false positive rate of Multi-armed bandits.

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Multi-armed bandits minimize regret when performing A/B tests, trading off between exploration and exploitation. Monte Carlo simulations shows that less exploration yields less statistical significance.

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PlanOut is a framework for online field experiments. It was created by Facebook in 2014 to make it easy to run and iterate on sophisticated experiments in a statistically sound manner.

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A/B tests go wrong all the time, even in sophisticated product teams. As this article shows, for a range of problems we can run automated validation checks to catch problems early, before they have too bad of an effect on customers or the business. These validation checks compare various statistical properties of the funnels A and B to catch likely problems. Large technology companies are running such validation checks automatically and continuously for their online experiments.

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I show using Monte Carlo simulations that randomizing user assignments into A/B test experiments makes it possible to run multiple A/B tests at once and measure accurate lifts on the same metric, assuming the experiments are independent.

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I compare probabilities from Bayesian A/B testing with Beta distributions to frequentist A/B tests using Monte Carlo simulations. Under a lot of circumstances, the bayesian probability of the action hypothesis being true and the frequentist p value are complementary.

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