## Retail-as-a-Service: Platform Innovation Enables Retailer-Defined Frictionless Commerce

In the reality of today’s disruption, the focus on rosy statistics about the growth of online eclipsing the power of the store experience is passe.

## 5 Ways Visual Search Will Change the Way You Market

Hubspot’s recent List of Marketing Statistics for 2019 identified an unmistakable trend — the importance of visual content on social media.
One of the highlighted studies found that the average person has the ability to recall 65% of the visual content they see almost three days later.

## Estimating a normal mean with a cauchy prior

The setup
When doing statistics the Bayesian way, we are sometimes bombarded with complicated integrals that do not lend themselves to closed-form solutions. This used to be a problem. Nowadays, not so much. This post illustrates how a person can use the Monte Carlo method (and R) to get a good estimate for an integral that might otherwise look unwieldy at first glance.

## Maiden voyage

Who
Me. I’m an associate professor of Statistics at Youngstown State University in Youngstown, Ohio, USA. I’ve been using R for about 7 years, Emacs about 3 years, git about 1 year, and Org-Mode for less than a year.
What
I want this blog to be about statistics, plain and sample. No frills, no tomfoolery, just bare-boned statistics from beginning to end.

## lies, damned lies, and social media statistics

Social media statistics – shares, retweets, and likes – reflect content’s value the way a funhouse mirror reflects one’s looks: grotesquely.  As the web lines its halls with social mirrors, these distortions are influencing the content we create and consume.

## To do: Construct a build-your-own-relevant-statistics-class kit.

Alexis Lerner, who took a couple of our courses on applied regression and communicating data and statistics, designed a new course, “Jews: By the Numbers,” at the University of Toronto:
But what does it mean to work with data and statistics in a Jewish studies course?

## Attention: Can we formalize it?

In statistics the bias-variance tradeoff is a core concept. Roughly speaking, bias is how well the best hypothesis in your hypothesis class would perform in reality, whereas variance is how much performance degradation is introduced from having finite training data. Abu-Mostafa has a nice lecture on this.

## Ergodicity In The World Of IoT

Ergodicity is one of the most important concepts in statistics. More importantly, it has a lot of real world applications. In this case, it’s applicable to the staggering number of internet connected devices in the world of Internet of Things (IoT). Most of the experiments conducted by research labs, businesses, and marketing agencies often rely on statistics to compile the results.

## Type Safety and Statistical Computing

I broadly believe that the statistics community would benefit from greater exposure to computer science concepts. Consistent with that belief, I argue in this post that the concept of type-safety could be used to develop a normative theory for how statistical computing systems ought to behave.

## Re-sampling: Amazing Results and Applications

This crash course features a new fundamental statistics theorem — even more important than the central limit theorem — and a new set of statistical rules and recipes.

## Using Bayesian Decision Making to Optimize Supply Chains

(c) 2019 Thomas Wiecki & Ravin Kumar
As advocates of Bayesian statistics in data science we often have to convince business-minded colleagues or customers of the added value of such an approach.

## Statistics – the rules of the game

What is statistics about, really? It’s easy to go through a class and get the impression that it’s about manipulating intimidating formulas. But what’s the goal of them? Why did people invent them?
If you zoom out, the big picture is more conceptual than mathematical. Statistics has a crazy, grasping ambition: it wants to tell you how to best use observations to make decisions.

## Exploring a new philosophy of statistics field

This article came out on Monday on our Summer Seminar in Philosophy of Statistics in Virginia Tech News Daily magazine.
October 28, 2019
.
From universities around the world, participants in a summer session gathered to discuss the merits of the philosophy of statistics. Co-director Deborah Mayo, left, hosted an evening for them at her home.

## The Challenge of Smart Data

Official statistics have never been exempt from the changes taking place around them. Numerous organisations at national and international level are constantly dealing with it and it is always interesting to see what the current 2019 assessment of future challenges is.

## Two Years Ago

He was a pioneer and a great inspiration for what public statistics always strives for: more visibility, more understanding and more resonance. Two years ago Hans Rosling (27 July 1948 – 7 February 2017) died too young.
Demanding and enriching was an encounter with Hans Rosling.

## You Need Statistics to Make Wine

The American Statistical Association has identified 146 college majors that require statistics to complete a degree. You probably wouldn’t be surprised that statistics is required for degrees in mathematics, engineering, physics, astronomy, chemistry, meteorology, and even biology and geology.

## Is “abandon statistical significance” like organically fed, free-range chicken?

The question: is good statistics scalable?
This comes up a lot in discussions on abandoning statistical significance, null-hypothesis significance testing, p-value thresholding, etc.