Crazy Research for the Work from Home Crowd

December 16, 2020

I read — despite my inner voice shouting, no, no, no — “Australian Study Shows Working in Pajamas Does Mot Hurt Productivity.” One summer session in graduate school, I had a roomie who slept without anything. Nifty, particularly when I had to observe this person sitting at the desk in the dorm before heading to class. Yeah, disgusting then and the memory is disgusting now.

The write up states:

When the study examined the effects wearing pajamas had on productivity and mental health, it found that wearing pajamas was associated with more frequent reporting of poorer mental health. For 59% of participants who wore pajamas during the day at least one day a week, they admitted their mental health declined while working from home, versus 26% of participants who did not wear pajamas while working from home.

The headline sort of misses the point.

But one of the flaws in the study is that the question, “Do you wear clothing when you sleep?” seems to have been ignored by the journalist and maybe the researchers in Sydney.

Key point: Pretty silly stuff. I want to know what percentage of the sample slept naked and then arose to work in a productive manner with a good mental attitude. Then I want to know that if a partner were present for the naked WFHers, what is the impact of this behavior on anyone able to look at this nude person perched in an Aeron with a laptop scrunched on their chest.

Got the picture?

Stephen E Arnold, December 16, 2020

Want to Manipulate Humans? Try These Hot Buttons

December 3, 2020

Okay, thumb typing marketers, insights from academia. Navigate to “We Are All Behavioral, More or Less: A Taxonomy of Consumer Decision Making.” The write up is available from Dartmouth, home of behavioral economists and psychologists and okay pizza.

The write up is 70 pages in length and chock full of jargon and academic thinking. Nevertheless, the author, one Victor Stango, reveals some suggestive information.

Here are a couple of examples:

Table 3. Correlations among behavioral biases, and between biases and other decision inputs offers insight into pairings of bias factors

Table 5. Rotated 8-factor models and loadings of decision inputs on common factors provides a “look up table” with values to help guide a sales pitch

The list of hot button factors includes:

  • Present bias
  • Choice type
  • Risk biases
  • Confidence
  • Math bias
  • Attention
  • Patience vs. risk aversion
  • Cognitive skills
  • Personality

Net net: Manipulate biases by combining factors. Launch those online marketing campaigns via social media with confidence, p-value lovers.

Stephen E Arnold, December 2, 2020

Surveys: These Marketing Devices Are Accurate, Right?

November 10, 2020

There’s nothing like a sample, a statistical sample, that is. What’s interesting is that the US polls seem to have been reflecting some interesting but marketing-type trends. The bastion of “real journalism”— the UK Daily Mail — published “…We Did a Good Job: Defiant Pollster Nate Silver Rushes to Defend His Profession after Another Systematic Failure of Polls in the Build-Up to an Election.” Bibliophiles will note that I have omitted the tasteful obscenity. I like to avoid using words likely to irritate the really smart software which edits blog posts.

The write up points out:

FiveThirtyEight founder and editor-in-chief Nate Silver hit back at those slamming the website for being so off with their election predictions.

Let’s think about why FiveThirtyEight and other polls seem to have predicted a reality different from the one generated by humanoids marking ballots.

First, there is the sample. Picking people at random is dependent on a number of factors: Sources, selection bias, humanoids who don’t respond, etc.

Second, there are the humanoids themselves. Some people plug in the “answers” which get the poll over with really fast. I lose interest at the first hint of dark patterns which make it tough to know how may questions I have to answer to get the coupon, pat on the head, or the free shopping sack.

Third, there is counting. Yep, humans or machine things can happen.

Fourth, there is analysis. It is remarkable what one can do when counting or doing “analytics.”

The Daily Mail quotes an expert about making polls better:

‘The polling profession needs to reshape and reorganize their questionnaires,’ Luntz [the polling expert] told ‘It’s the only way they’ll ever get it right.’

But I keep thinking about the FiveThirtyEight obscenity. Defensive? Eloquent? Subjective? Insightful?

That subjective thing.

Stephen E Arnold, November 10, 2020

Spreadsheet Fever Case Example

October 12, 2020

I have been using the phrase “spreadsheet fever” to describe the impact of fiddling with numbers in Microsoft Excel has on MBAs. With Excel providing the backbone for numerous statistical confections, the sugar hit of magic assumptions cannot be under-estimated. The mental structure of a crazed investment analyst brooks no interference from common sense.

Excel: Why Using Microsoft’s Tool Caused Covid-19 Results to Be Lost” provides a possible case example of what happens when thumbtypers and over-confident innumerates tangle with a digital spreadsheet. No green eyeshades and no pencils needed. Calculators? One can hear a 22 year old ask, “What’s a calculator? I have one on my iPhone?”

The Beeb reports:

PHE [Public Health England, a fine UK entity] had set up an automatic process to pull this data together into Excel templates so that it could then be uploaded to a central system and made available to the NHS Test and Trace team, as well as other government computer dashboards.

And what tool did these over confident wizards use?

Microsoft Excel, the weapon of choice for business and STEM analysis, of course.

How did the experts wander off the information highway into a thicket of errors? The Beeb explains:

The problem is that PHE’s own developers picked an old file format to do this – known as XLS. As a consequence, each template could handle only about 65,000 rows of data rather than the one million-plus rows that Excel is actually capable of. And since each test result created several rows of data, in practice it meant that each template was limited to about 1,400 cases. When that total was reached, further cases were simply left off.

The fix? Can kicking perhaps:

But insiders acknowledge that the current clunky system needs to be replaced by something more advanced that excludes Excel, as soon as possible.


Stephen E Arnold, October 12, 2020

Math Cheat Sheets

October 9, 2020

Since we live in a statistics charged world, there is a strong likelihood that you may want to brush up on math. Fear not. A collection of math cheat sheets are available without charge. “Probability Cheat Sheet – Harvard University” includes some links (good and bad). What does a cheat sheet from Harvard University with its modest endowment and measly seven percent return so far this year look like? Here’s a sample from the probability document:


Stephen E Arnold, October 9, 2020

9 21

September 20, 2020

One of the DarkCyber research team came across this chart on the Datawrapper Web site. Datawrapper provides millennial-ready analysis tools. With some data and the firm’s software, anyone can produce a chart like this one with green bars for negative numbers.

datawrapper chicago

What is the chart displaying. The odd green bar shows the decline in job postings. Why green? No idea. What is the source of the data? Glassdoor, a job listings site. The data apply only to Chicago, Illinois. The time period is August 2020 versus August 2019. The idea is that the longer the bar, the greater the decline. Why is the bar green? Isn’t red a more suitable color for negative numbers?

Shown in this image are the top 12 sectors for job loss. To be clear, the longer the bar, the fewer job postings. Fewer job postings, one assumes, translates to reduced opportunities for employment.

What’s interesting is that accounting, consulting, information technology, telecommunications, and computer software and hardware are big losers. Those expensive MBAs, the lost hours studying for the CPA examination, and thumb typing through man pages are gone for now.


  • The colors? Red maybe.
  • The decline in high technology work and knowledge work is interesting.
  • The “open jobs” numbers are puzzling. Despite declines, Chicago – the city of big shoulders and big challenges – has thousands of jobs in declining sectors.

Net net: IT and computer software and hardware look promising. The chart doesn’t do the opportunities justice. And the color?

Stephen E Arnold, September 20, 2020

Social Science: Like Astrology and Phrenology Perhaps?

September 15, 2020

I do not understand sociology. In 1962, I ended up in a class taught by an esteemed eccentric named Bruce Cameron, Ph.D. I had heard about his interest in short wave and drove past his home to observe the bed springs hanging on the front of his house. The idea, as I recall, was to improve radio reception. Those in the engineering department at the lousy university I attended shared the brilliant professor’s fascination with commercial bed technology at lunch. Even I as a clueless freshman (or is it now freshperson?) knew about the concept of buying an antenna from our local electronics shop.

In the remarkable Dr. Cameron’s Sociology 101 class, he posed the question, “Why do Eskimos wear mittens?” Today, the question would have to reference indigenous circumpolar  people or another appropriate term. But in 1962, Eskimos was the go-to word.

I pointed out that I had seen in the Smithsonian Museum an exhibit of Eskimo hand wear and that there were examples of mittens with a finger component (trigger mits or nord gauntlets), thus combining the warmth of a mitten with the needed dexterity to remove a harpoon from a baby seal.

He ignored my comment. The question turned up on our first examination, and I recycled my alleged learning from the Smithsonian information card for the exhibit.

I received zero credit for my answer. Bummer. I think that was the point at which I dismissed “sociology” and placed it and the good professor in the same pigeon hole I used for astrology and phrenology.

After reading “What’s Wrong with Social Science and How to Fix It: Reflections After Reading 2578 Papers,” I reaffirmed my skepticism of sociology and its allied fields:

But actually diving into the sea of trash that is social science gives you a more tangible perspective, a more visceral revulsion, and perhaps even a sense of Lovecraftian awe at the sheer magnitude of it all: a vast landfill—a great agglomeration of garbage extending as far as the eye can see, effluvious waves crashing and throwing up a foul foam of p=0.049 papers.

The write up contains some interesting data. In reference to a citation graph, the paper points out why references to crappy research persist:

As in all affairs of man, it once again comes down to Hanlon’s Razor. Either:

  1. Malice: they know which results are likely false but cite them anyway.
  2. or, Stupidity: they can’t tell which papers will replicate even though it’s quite easy.

There is another reason: Clubs of so-called experts informally coordinate or simply do the “I will scratch your back if you scratch mine.”

What quasi-sociological field is doing its best to less corrupt? Surprisingly, it is economics. Education seems to have some semblance of ethical behavior, at least based on this sample of papers. But maybe the sample is skewed.

The paper concludes with a list of suggestions. Useful, but I think the present pattern of lousy work is going to persist and increase.

Hang those bed springs on the side of the house. Works for “good enough” solutions.

Stephen E Arnold, September 15, 2020

Expertise Required: Interesting Assertion

September 14, 2020

One of the DarkCyber research team spotted “Lack of Expertise Is the Biggest Barrier for Implementing IoT Solutions.” The surprising assertion comes from Claris, an outfit owned by Apple. Claris (once known as FileMaker Inc.). Clear? Clear as Claris.

The information in the write up presents an interesting assertion about the Internet of Things. An IoT device is a mobile phone or a gizmo that connects to the Internet; for example, an Anduril surveillance drone.

The interesting parts are the actual factual statements; for example:

  • 20 percent of “SMB leaders worry about security and privacy when implementing IoT. Furthermore, they don’t clearly see the return on investment.”
  • 67 percent believe IoT could bring them a competitive advantage and are saying their competitors are “doing more” with IoT at the time.
  • “SMB leaders mentioned improved efficiency, productivity and speed, while about a third see gathering business intelligence as the main driver towards IoT adoption.”
  • About 33 percent say “it’s likely their SMB will launch an IoT initiative within the next three years, while almost half added that their company was lagging behind the competition.”
  • 24 percent) stated their project already yielded ROI, while 38 percent expect it to happen within a year.

Do we know the details of the study, the sample size, the methodology used to select those surveyed, or the statistical validity of the data? Of course not. That is what makes the fact so interesting. That and the need for “expertise.” Perhaps the data were tallied in Filemaker?

Stephen E Arnold, September 14, 2020

Be Smart: Live in Greenness

August 27, 2020

I do not want to be skeptical. I do not want to suggest that a study may need what might be called verification. Please, read “Residential Green Space and Child Intelligence and Behavior across Urban, Suburban, and Rural Areas in Belgium: A Longitudinal Birth Cohort Study of Twins.” To add zip to your learning, navigate to a “real” news outfit’s article called “Children Raised in Greener Areas Have Higher IQ, Study Finds.” Notice any significant differences.

First, the spin in the headline. The PLOS article points out that the sample comes from Belgium. How representative is this country when compared to Peru or Syria? How reliable are “intelligence” assessments? What constitutes bad behavior? Are these “qualities” subject to statistically significant variations due to exogenous factors?

I don’t want to do a line by line comparison of the write up which wants to ring the academic gong. Nor do I want to read how “real” journalists deal with a scholarly article.

I would point out this sentence in the scholarly article:

To our knowledge, this is the first study investigating the association between residential green space and intelligence in children.

Yeah, let’s not get too excited from a sample of 620 in Belgium. Skip school. Play in a park or wander through thick forests.

Stephen E Arnold, August 27, 2020

Bias in Biometrics

August 26, 2020

How can we solve bias in facial recognition and other AI-powered biometric systems? We humans could try to correct for it, but guess where AI learns its biases—yep, from us. Researcher Samira Samadi explored whether using a human evaluator would make an AI less biased or, perhaps, even more so. We learn of her project and others in Biometric’s article, “Masks Mistaken for Duct Tape, Researchers Experiment to Reduce Human Bias in Biometrics.” Reporter Luana Pascu writes:

“Curious to understand if a human evaluator would make the process fair or more biased, Samadi recruited users for a human-user study. She taught them about facial recognition systems and how to make decisions about system accuracy. ‘We really tried to imitate a real-world scenario, but that actually made it more complicated for the users,’ Samadi said. The experiment confirmed the difficulty in finding an appropriate dataset with ethically sourced images that would not introduce bias into the study. The research was published in a paper called A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition.”

Many other researchers are studying the bias problem. One NIST report found a lot of software that produced 10-fold to 100-fold increase in the probability of Asian and African American faces being inaccurately recognized (though a few systems had negligible differences). Meanwhile, a team at Wunderman Thompson Data found tools from big players Google, IBM, and Microsoft to be less accurate than they had expected. For one thing, the systems had trouble accounting for masks—still a persistent reality as of this writing. The researchers also found gender bias in all three systems, even though the technologies used are markedly different.

There is reason to hope. Researchers at the Durham University’s Computer Science Department managed to reduce racial bias by one percent and improve ethnicity accuracy. To achieve these results, the team used a synthesized data set with a higher focus on feature identification. We also learn:

“New software to cut down on demographic differences in face biometric performance has also reached the market. The ethnicity-neutral facial recognition API developed by AIH Technology is officially available in the Microsoft Azure Marketplace. In March, the Canadian company joined the Microsoft Partners Network (MPN) and announced the plans for the global launch of its Facial-Recognition-as-a-Service (FRaaS).”

Bias in biometrics, and AI in general, is a thorny problem with no easy solution. At least now people are aware of the issue and bright minds are working to solve it. Now, if only companies would be willing to delay profitable but problematic implementations until solutions are found. Hmmm.

Cynthia Murrell, August 26, 2020

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