2010 Experimentation Measurement Testing 
Data Science 101
JANUARY 15, 2020
Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Reliability: It means measurements should have repeatable results. For eg: you measure the blood pressure of a person.
The Unofficial Google Data Science Blog
JULY 22, 2020
Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.
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Occam's Razor
SEPTEMBER 15, 2014
In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. But why blame others, in this post let's focus on one important reason whose responsibility can be squarely put on your shoulders and mine: Measurement.
Occam's Razor
MARCH 23, 2010
Too many new things are happening too fast and those of us charged with measuring it have to change the wheels while the bicycle is moving at 30 miles per hour (and this bicycle will become a car before we know it – all while it keeps moving, ever faster). Usually at least a test. And I doubt it is going to happen soon.
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We must correct for multiple hypothesis tests. Make experimentation cheap and understand the cost of bad decisions. We ought not dredge our data.
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