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. Bias can cause a huge error in experimentation results so we need to avoid them. Validity: Valid data measures what we actually intend to find out.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. He also encouraged data scientists to understand how new data science algorithms rearrange power as well as how the history of data is a story of truth and power.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

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.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Although these difficulties are more pronounced when we deal with observational data, the proliferation of hypotheses and lack of intentionality in data collection can even impact designed experiments. Be sure to have a deep, thorough understanding of how data under consideration was collected and what it actually means.

Experimentation 61

Experimentation Testing Statistics Metrics 61