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Frank Hopkins

As seen in: Irish Independent, Towards Data Science
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Articles by Frank Hopkins

Propensity Score Matching (PSM) for A/B Testing: Reducing Bias in Observational Studies

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By Frank Hopkins, San Fran
| Towards Data Science
A comprehensive guide to implementing PSM with your experimental data, including Python code A/B testing is a widely used experimental design in which two or more interventions are compared on an outcome of interest. The goal of A/B testing is to estimate the causal effect of the interventions on the outcome, while controlling for potential confounding variables.
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Detecting Sample Ratio Mismatch in your A/B Tests

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By Gartner Data, Frank Hopkins
| Towards Data Science
This post helps you identify whether you have unbalanced cohorts in your digital experiments and incorporates some basic Python functions to aid in SRM detection Image created by Author (Frank Hopkins) Introduction When an experiment is paused and we look to investigate the statistics of a given test we often ponder on our findings.
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The Arsenal Right Back Conundrum

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By Frank Hopkins
| dailyafc.com
It has long been discussed that Arsenal require a new starting right-back. However, it does seem that links we were seeing at the beginning of the window seem to have tempered.This is down to a couple of reasons. Firstly, we are currently prioritising other areas and secondly, we are looking to sell Bellerin. The latter point makes sense, if we don’t get Bellerin off the books and get a new right-back in, we will have four players in that position (including Bellerin, Chambers and Cedric).
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CUPED R-Shiny Tool

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By Frank Hopkins
| Towards Data Science
Introduction to CUPED Recently I have been delving into ways in which experiment sensitivity can be increased — via reduction of variance associated with pre-experiment information. If a strong correlation can be determined between a pre-experiment metric (covariate) and experimental metric, the variance that exists within your experimental metric prior to conducting your experiment can be controlled for, prior to conducting any significance testing.
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