CIO Business Intelligence

JUNE 28, 2022

More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis. Others are philosophical, testing our ability to reason about abstract qualities. Good data scientists can also reduce some of this uncertainty through cleansing.

Management 138

Management Internet of Things Statistics Data-driven 138

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty. This has serious implications for software testing, versioning, deployment, and other core development processes.

Management 332

Management Machine Learning Experimentation Metrics 332

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. This is quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

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 100

Cost-Benefit Modeling Manufacturing Interactive 100

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

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

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature. Identify, hypothesize, test, react. But at the same time, they had to have a real test of an actual feature. You don’t need a beautiful beast to go out and test.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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

The Unofficial Google Data Science Blog

JULY 18, 2023

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. But for more complicated metrics like xRR, our preference is to bootstrap when measuring uncertainty.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 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

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test. After the test period finishes, the campaigns in the treatment group are reset to their original configurations.

Advertising 42

Advertising Testing Sales Statistics 42

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.

Forecasting 52

Forecasting Uncertainty Measurement Software 52

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

Domino Data Lab

JUNE 23, 2019

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. As quickly as possible, you want to get designers and data scientists, machine learning folks together and using real data and prototyping and testing.

Management 110

Management Machine Learning Experimentation Metrics 110

Insight

FEBRUARY 19, 2020

I held out 20% of this as a test set and used the remainder for training and validation. Below is the result of a single XGBoost model trained on 80% of the data and tested on the unseen held-out 20%. Scatterplot of the predicted ROI vs. the true ROI for the hold-out test set. Even then, some manual cleaning was needed (e.g.,

Risk 67

Risk ROI Modeling Metrics 67

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

The beliefs of this community are always evolving, and the process of thoughtfully generating, testing, refuting and accepting ideas looks a lot like Science. 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

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

APRIL 16, 2019

Such decisions involve an actual hypothesis test on specific metrics (e.g. 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. Are the potential improvements realized and worthwhile?

Experimentation 68

Experimentation Sales Metrics Measurement 68

CIO Business Intelligence

FEBRUARY 12, 2024

As genAI caught fire in 2023, many organizations rushed to test and learn from the technology and harness it to grow productivity and improve processes. 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.

IT 137

IT Consulting Cost-Benefit Uncertainty 137

datapine

SEPTEMBER 4, 2023

Your Chance: Want to test a powerful data visualization software? Your Chance: Want to test a powerful data visualization software? The pioneering visual is regarded by many as the “greatest statistical graphic ever drawn,” and while such a statement is subjective, it’s nothing short of inspirational.

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