Remove 2011 Remove Experimentation Remove Reporting Remove Statistics
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Unintentional data

The Unofficial Google Data Science Blog

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

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Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Occam's Razor

If you are open to being challenged… then here are the short-stories inside this post… The World Needs Reporting Squirrels. The World Needs Reporting Squirrels. If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis. Bonus #2: The Askers-Pukers Business Model.

Modeling 127
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Estimating causal effects using geo experiments

The Unofficial Google Data Science Blog

A geo experiment is an experiment where the experimental units are defined by geographic regions. Statistical power is traditionally given in terms of a probability function, but often a more intuitive way of describing power is by stating the expected precision of our estimates. They are non-overlapping geo-targetable regions.

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Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates!

Occam's Razor

I mean developing and inserting a subtle collection of gentle nudges that can help increase the conversion rate by a statistically significant amount. Go to the Multi-Channel Funnels folder in Google analytics and look at two other yummy reports: Time Lag and Path Length. I don’t mean: BUY IT NOW OR ELSE! Sizing the Opportunity.

Strategy 124
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Remember that the raw number is not the only important part, we would also measure statistical significance. Online, offline or nonline.

Metrics 156