The Unofficial Google Data Science Blog

JULY 18, 2023

While we can count the number of rods that measure a particular length, we struggle to provide a concrete reference value for something like the liberalness of a particular government. While a measurement like weight seems to be pretty concrete, a variety of factors can introduce uncertainty about the “true value” that you’re measuring.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Jen Stirrup

NOVEMBER 15, 2022

The RPA market may grow to $25 billion in 2025 according to Forrester, and it has the promise of supporting digital transformation through streamlining digital transformation ( Reference ). From a data science perspective, it is possible to begin immediately by framing problems regarding machine learning or statistical issues.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

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

Insight

MAY 15, 2020

In this time of terrifying uncertainty, some might focus on their own career journey over others. Wishlists are especially off-putting to women, who statistically will only apply to job opportunities if they meet 100% of the listed requirements, versus men applying when they meet 60%. However, many are looking out for their colleagues.

Uncertainty 76

Uncertainty Data Science Statistics Marketing 76

The Unofficial Google Data Science Blog

AUGUST 27, 2019

But importance sampling in statistics is a variance reduction technique to improve the inference of the rate of rare events, and it seems natural to apply it to our prevalence estimation problem. References [1] Art Owen. Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4] High Risk 10% 5% 33.3%

Metrics 98

Metrics Statistics Uncertainty Optimization 98

The Unofficial Google Data Science Blog

JULY 22, 2020

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

IBM Big Data Hub

APRIL 18, 2024

AGI, sometimes referred to as strong AI , is the science-fiction version of artificial intelligence (AI), where artificial machine intelligence achieves human-level learning, perception and cognitive flexibility. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

Peter James Thomas

AUGUST 12, 2018

Integrity of statistical estimates based on Data. Having spent 18 years working in various parts of the Insurance industry, statistical estimates being part of the standard set of metrics is pretty familiar to me [7]. The thing with statistical estimates is that they are never a single figure but a range. million ± £0.5

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.

Forecasting 64

Forecasting Statistics Modeling Software 64

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution.

Modeling 40

Modeling Statistics Advertising Testing 40

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

MAY 31, 2016

Such regions are often referred to as Generalized Market Areas (GMAs) or simply geos. We often refer to this as the Return On Ad Spend (ROAS). 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.

Advertising 42

Advertising Testing Sales Statistics 42

Peter James Thomas

JUNE 20, 2019

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1] , but I found the above figures striking. King was a wise King, but now he was gripped with uncertainty. Source: Google Trends. The office of Chief Wizard commanded a stipend that was not inconsiderable.

Cost-Benefit 55

Cost-Benefit Data Warehouse Consulting Data Science 55

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. References [1] Hyndman, Rob J., Journal of Official Statistics 6.1 We hope this blog post is the start of such a dialogue. OTexts, 2014.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

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. References [1] Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott E.,

IT 68

IT Visualization Modeling Uncertainty 68

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

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

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

CIO Business Intelligence

FEBRUARY 12, 2024

Such bleak statistics suggest that indecision around how to proceed with genAI is paralyzing organizations and preventing them from developing strategies that will unlock value. Even as organizations plan to boost spending on genAI in 2024. Even as organizations plan to boost spending on genAI in 2024.

IT 124

IT Consulting Cost-Benefit Uncertainty 124

O'Reilly on Data

JUNE 16, 2020

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. Duke refers to this as resulting. The decision-making process was fine.

Uncertainty 273

Uncertainty Testing Risk Reporting 273