## Data Literacy – The Chainsaw Case

A famous business school case by Harvard Professor Michael Porter on forecasting chainsaw sales dramatically illustrated the limits of statistical models when common business sense and clear-eyed thinking are missing. In the chainsaw case, students were asked to forecast the future U.S. demand for chainsaws, a growing market, and assess the relative positions of differentContinue reading “Data Literacy – The Chainsaw Case”

## AI Success, But Not Business Success

In their book, “Mining Your Own Business,” Jeff Deal and Gerhard Pilcher, COO and CEO of Elder Research respectively, describe what I’ll call “The Case of the Climbing Churn.” Churn is when a subscriber cancels or fails to renew a service or subscription. A successful predictive model for identifying likely churners was deployed for aContinue reading “AI Success, But Not Business Success”

## Why Statisticians Like Odds

In your introductory statistics class, probability took center stage. Odds were for gamblers. But it turns out odds play an important role in statistics, too. The relationship between the two is simple. To estimate the probability that event “A” will happen, we divide the number of times A occurs by the number of all events,Continue reading “Why Statisticians Like Odds”

## Student Spotlight – Thomas Karagiorgios

He spoke with Val Woodside, our Customer Success Specialist at Statistics.com about his experience in the certificate program. Tell us about your experience at Statistics.com? It was excellent. For me it was the first time that I studied through this online process. You have to study on your own and then you can send inContinue reading “Student Spotlight – Thomas Karagiorgios”

## Controlling Leaks

Good psychics have a knack for getting their audience to reveal, unwittingly, information that can be turned around and used in a prediction.  Statisticians and data scientists fall prey to a related phenomenon, leakage, when they allow into their models highly predictive features that would be unavailable at prediction time.  In one noted example, aContinue reading “Controlling Leaks”

## As an Aspiring Data Scientist, What Do I Really Need to Know About Statistics?

As the popularity of data science has grown, so too has advice on how to get jobs in data science.  A common form of advice is a list of sample questions you might be asked at your job interview (see here and here for examples).  Often, the list starts out with statistics, but beware: itContinue reading “As an Aspiring Data Scientist, What Do I Really Need to Know About Statistics?”

## False Positive Rate – It’s Not What You Might Think

“A little knowledge is a dangerous thing,” said Alexander Pope in 1711; he could have been speaking of the use of statistics by experts in all fields. In this article, we look at three consequential mistakes in the field of statistics. Two of them are famous, the third required a deep dive into the corporate annual reports of

## Famous Errors in Statistics

“A little knowledge is a dangerous thing,” said Alexander Pope in 1711; he could have been speaking of the use of statistics by experts in all fields. In this article, we look at three consequential mistakes in the field of statistics. Two of them are famous, the third required a deep dive into the corporate annual reports of