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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. And we can keep repeating this approach, relying on intuition and luck. Why experiment with several parameters concurrently?

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown.

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The 2015 Digital Marketing Rule Book. Change or Perish.

Occam's Razor

AND you can have analysis of your risk in almost real time to get an early read and in a few days with statistical significance! Allocate some of your aforementioned 15% budget to experimentation and testing. The 2015 Digital Marketing Rule Book. You can literally control for risk should everything blow up in your face.

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Estimating causal effects using geo experiments

The Unofficial Google Data Science Blog

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. A geo experiment is an experiment where the experimental units are defined by geographic regions. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test.

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The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. We use the diagnostic test results of our regression model to support the reasons why CIs should not be used in financial data analyses. and an error term ??

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means.