The Unofficial Google Data Science Blog

DECEMBER 4, 2019

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. These characteristics of the problem drive the forecasting approaches.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Jen Stirrup

NOVEMBER 15, 2022

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Of course, the findings need to add value, but how do we measure this success? Measures can be financial, tying in with the business strategy. After all, it can sound a bit woolly!

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Data Science and Beyond

JANUARY 7, 2019

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Don’t compare confidence intervals visually. Pitfall #1: Inaccurate confidence intervals.

Statistics 77

Statistics Uncertainty Testing Modeling 77

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. All descriptive statistics can be calculated using quantitative data. Digging into quantitative data. or “how often?”

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Perceptual Edge

APRIL 21, 2019

We could argue that the signal-to-noise ratio is the most essential consideration in data visualization—the fundamental guide for all design decisions while creating a data visualization and the fundamental measure of success once it’s out there in the world.

Visualization 76

Visualization Statistics Uncertainty Modeling 76

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

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? It is hard to account for such tweaking in measurement systems. Some relate to inherent issues with what is being measured. million.

Metrics 49

Metrics Statistics Data Quality Measurement 49

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The AGI would need to handle uncertainty and make decisions with incomplete information. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

Cost-Benefit 88

Cost-Benefit Modeling Manufacturing Interactive 88

Insight

FEBRUARY 19, 2020

The genre uniqueness is a measure of how unique a movie’s combination of genre categories is relative to all movies in my data set. I trained 500 models on these 500 random subsamples and built a distribution of ROI values from which I can extract summary statistics such as the median and 95% confidence interval.

Risk 67

Risk ROI Modeling Metrics 67

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. Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4] High Risk 10% 5% 33.3% How Many Strata? 16 (2): 101–133. [3]

Metrics 98

Metrics Statistics Uncertainty Optimization 98

The Unofficial Google Data Science Blog

MAY 31, 2016

It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

Occam's Razor

APRIL 8, 2013

First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Measure and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. Crucially, our approach does not rely on model performance on holdout samples.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

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

APRIL 16, 2019

On the other hand, fledgling products often have neither the statistical power to identify the effects of small incremental changes, nor the luxury to contemplate small improvements. The metrics to measure the impact of the change might not yet be established. If so, decision making is further simplified.

Experimentation 68

Experimentation Sales Metrics Measurement 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. These measurement-obsessed companies have an advantage when it comes to AI.

Management 110

Management Machine Learning Experimentation Metrics 110

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. 2009, " Measuring invariances in deep networks ". CoRR, 2016. [3] Goodfellow, Quoc V.

IT 68

IT Visualization Modeling Uncertainty 68

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Note also that this account does not involve ambiguity due to statistical uncertainty. As you can see from the tiny confidence intervals on the graphs, big data ensured that measurements, even in the finest slices, were precise. We addressed #1 with an observational study and #2 with a randomized experiment, as follows.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

BI-Survey

FEBRUARY 14, 2022

Overnight, the impact of uncertainty, dynamics and complexity on markets could no longer be ignored. Local events in an increasingly interconnected economy and uncertainties such as the climate crisis will continue to create high volatility and even chaos. The COVID-19 pandemic caught most companies unprepared. BARC Recommendations.

Forecasting 52

Forecasting Uncertainty Measurement Software 52

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

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. The ESA’s Hipparcos was the first space astrometry mission, which operated from 1898 to 1993.

Interactive 264

Interactive Visualization Cost-Benefit Dashboards 264

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. Mike had made the common error of equating a bad outcome with a bad decision.

Uncertainty 275

Uncertainty Testing Risk Reporting 275