2010 Experimentation Measurement 
Data Science 101
JANUARY 15, 2020
Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV. Reliability: It means measurements should have repeatable results. For eg: you measure the blood pressure of a person.
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.
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The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
Occam's Razor
JULY 26, 2010
If today you are a content site that is only focused on measuring content consumed try to go deeper to understanding CPA of the ads or Visitor Loyalty. 3: Measure complete site success. Measure everyone's success. But donations is just one measure of success (" macro conversion "). So why not measure those?
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). our measurement strategies 2. success measures. Likely not.
CIO Business Intelligence
AUGUST 14, 2024
Measuring costs and value The other major issue with gen AI is the price. After the excitement and experimentation of last year, CIOs are more deliberate about how they implement gen AI, making familiar ROI decisions, and often starting with customer support. But experimentation to achieve significant results takes time.
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
We data scientists now have access to tools that allow us to run a large numbers of experiments, and then to slice experimental populations by any combination of dimensions collected. Make experimentation cheap and understand the cost of bad decisions. This leads to the proliferation of post hoc hypotheses. What is to be done?
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