The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

APRIL 23, 2024

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. Crucially, it takes into account the uncertainty inherent in our experiments.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

JULY 22, 2019

I definitely want to provide some shout-outs. In data science, definitely, there are other people who’ve talked more about that and we’ll point to them. He was saying this doesn’t belong just in statistics. I definitely recommend Chris as well. You’ve seen Jon up here, too, doing fantastic MC-ing.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Timo Elliott

JANUARY 18, 2021

We have definitely heard feedback from our employees that they would like some more flexibility in how they work. Data has always been important for organizations, but now, faced with so much uncertainty, everybody is interested in having more. Kurt: Yeah, that’s a really, really good point.

IT 76

IT Dashboards Digital Transformation Data-driven 76

The Unofficial Google Data Science Blog

JULY 18, 2023

Reliability and validity: Definitions and measurement Two fundamental concepts emerged from this research: reliability and validity. Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Occam's Razor

APRIL 8, 2013

The word hypothesis means a lot of different things, but in this context I like this definition from Wikipedia the best: People refer to a trial solution to a problem as a hypothesis, often called an "educated guess”, because it provides a suggested solution based on the evidence. The result? The graph is impressive, right?

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Peter James Thomas

AUGUST 12, 2018

Let’s start however with some definitions. Often an issue may also be a lack of master data definitions, so that similar data is labelled differently in different systems. Are there any weaknesses in this definition, for example is it sensitive to any glitches in – say – the tagging of Repeat Business?

Metrics 49

Metrics Statistics Data Quality Measurement 49

O'Reilly on Data

DECEMBER 9, 2019

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

Statistics 27

Statistics Uncertainty Risk Marketing 27

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. Let us briefly examine the need for the baseline in the definition of the attribution problem.

IT 68

IT Visualization Modeling Uncertainty 68

Domino Data Lab

JUNE 23, 2019

All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples. I think definitely in consumer companies they are real. This applies to algorithms and design.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

By definition, it is hard to disentangle causes and effects in these settings because it is rarely possible to isolate all relevant factors. Note also that this account does not involve ambiguity due to statistical uncertainty. the power grid, a streaming music service, the human body, the weather).

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

datapine

SEPTEMBER 4, 2023

16) Interactive Visualization Of The Exponential Spread Of COVID-19 **click image for source** The COVID-19 pandemic paralyzed the entire world with fear and uncertainty, probably more than any other event we’ve experienced in the past few decades.

Interactive 264

Interactive Visualization Cost-Benefit Dashboards 264