Marton Trencseni - Sun 25 April 2021 • Tagged with ab-testing
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.
Marton Trencseni - Wed 03 March 2021 • Tagged with ab-testing
I train a Pytorch Wasserstein MNIST GAN on Google Colab to beautiful MNIST digits.
Marton Trencseni - Tue 02 March 2021 • Tagged with ab-testing
I train a Pytorch Classic MNIST GAN on Google Colab to generate MNIST digits.
Marton Trencseni - Thu 17 December 2020 • Tagged with ab-testing, trump, politics
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.
Marton Trencseni - Thu 03 December 2020 • Tagged with ab-testing
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.
Marton Trencseni - Fri 21 August 2020 • Tagged with ab-testing
I use Monte Carlo simulations to explore the false positive rate of Multi-armed bandits.
Marton Trencseni - Fri 07 August 2020 • Tagged with ab-testing
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.
Marton Trencseni - Fri 22 May 2020 • Tagged with ab-testing
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.
Marton Trencseni - Thu 16 April 2020 • Tagged with ab-testing
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.
Marton Trencseni - Mon 06 April 2020 • Tagged with ab-testing
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.
Marton Trencseni - Tue 31 March 2020 • Tagged with bayesian, ab-testing
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.
Marton Trencseni - Mon 23 March 2020 • Tagged with ab-testing
The G-test for conversion A/B tests is similar to the Chi-squared test. Monte-Carlo simulations show that the two are indistinguishable in practice.
Marton Trencseni - Sat 21 March 2020 • Tagged with ab-testing
I use Monte Carlo simulations to explore how A/B testing on Watts–Strogatz random graphs depends on the degree distribution of the social network.
Marton Trencseni - Mon 09 March 2020 • Tagged with ab-testing
I use Monte Carlo simulations to show that experimentation on social networks is a beatiful statistical problem with unexpected nuances due to network effects.
Marton Trencseni - Thu 05 March 2020 • Tagged with ab-testing
Increased false positive rate due to early stopping is beautiful nuance of statistical testing. It is equivalent to running at an overall higher alpha. Data scientists need to be aware of this phenomenon so they can control it and keep their organizations honest about their experimental results.
Marton Trencseni - Tue 03 March 2020 • Tagged with ab-testing
Fisher’s exact test directly computes the same p value as the Chi-squared test, so it does not rely on the Central Limit Theorem to hold.
Marton Trencseni - Fri 28 February 2020 • Tagged with ab-testing
In an ealier post, I wrote about A/B testing conversion data with the Z-test. The Chi-squared test is a more general test for conversion data, because it can work with multiple conversion events and multiple funnels being tested (A/B/C/D/..).
Marton Trencseni - Sun 23 February 2020 • Tagged with ab-testing
The t-test is better than the z-test for timespent A/B tests, because it explicitly models the uncertainty of the variance due to sampling. Using Monte-Carlo simulations I show that around N=100, the t-test becomes the z-test.
Marton Trencseni - Sat 15 February 2020 • Tagged with ab-testing
I discuss the Z-test for A/B testing and show how to compute parameters such as sample size from first principles. I use Monte Carlo simulations to validate significance level and statistical power, and visualize parameter scaling behaviour.
Marton Trencseni - Thu 06 February 2020 • Tagged with data, ab testing, statistics
In the previous post, I talked about the importance of the Central Limit Theorem (CLT) to A/B testing. Here we will explore cases when we cannot rely on the CLT to hold.
