Accounting Fraud. Why you need to hug a good accountant

An accountant is having difficulty sleeping and goes to see his doctor. 

“Doctor, I just can’t get to sleep at night.” 

“Have you tried counting sheep?” 

“That’s the problem. I make a mistake and then spend three hours trying to find it.”

Just another accounting Joke!

Let me introduce you to the warriors who are battling accounting fraud. You will look at accounting differently and want to hug a good accountant.

Fraud is a significant global problem.

I believe that if we cut fraud in half, the savings would exceed the cost of eliminating poverty.

A 2021 study by John Guyton and Patrick Langetieg of the IRS, with Daniel Reck, Max Risch and Gabriel Zucman, published as a National Bureau of Economic Research (NBER) working paper, says, “We estimate that 36% of federal income taxes unpaid are owed by the top 1% and that collecting all unpaid federal income tax from this group would increase federal revenues by about $175 billion annually.”

That $175 billion, Princeton sociologist Matthew Desmond said, “is just about enough to pull everyone out of poverty.”

And just as an aside, unpaid taxes are not all fraud. The authors count honest mistakes too, and their December 2023 revision puts all unpaid federal income tax at about $300 billion a year (in 2012 dollars, averaged over 2008 to 2013).

Accounting fraud is a subset of fraud cases.

It is challenging to measure but may be even more important than the rest.

Every central policy or business decision rests on the assumptions of good economic data.

Investors, businesses, and governments all make decisions based on the assumption that the data has financial integrity.

We assume accounting fraud is negligible.

We must assume that because our entire economic system relies on that assumption.

In this piece, I will introduce you to some fearless warriors working to minimize accounting fraud.

When finished, you will want to hug a good accountant.

Read on to find out why.

Your tour guide

I have always valued accounting skills and the importance of integrity in the profession.

After all, my undergraduate degree was a BBA Major in Accounting, with minors in Computer Science and Mathematics, all Summa Cum Laude from Baruch College. 

After that, I spent two years teaching accounting as an Associate Instructor at Indiana University in Bloomington while I completed my MBA.

Our Knights, the Accountants

Professor Abe Briloff – Baruch College

The late Professor Abraham J. Briloff of Baruch College, a prominent accounting critic, was famous for his writings when I started at Baruch. 

Back in the 1960s, he had warned about the abusive use of “pooling of interest,” a creative accounting method acquirers could use to manipulate future earnings. 

The financial industry should have caught on to his brilliance immediately. 

However, the Financial Accounting Standards Board did not end pooling of interest accounting until 2001, decades after his warning. 

Despite the slow adoption of his ideas, he had a more instant impact as he wrote pieces on companies frequently published in Barron’s. As Floyd Norris wrote in The New York Times, “His articles often caused stock prices to plunge, never to recover.”

NY Times: Abe Briloff, an Accountant Who Saw Through the Games

Professor Seymour Eisenman – Baruch College

The late and great Professor Seymour Eisenman gave me my first introduction to accounting. 

He was the first person to earn a Ph.D. in accountancy from CUNY under the supervision of legendary Professor Briloff.

Professor Eisenman wanted his students to learn the craft profoundly. Most importantly, he cared about us deeply and wanted us to be good people. 

It wasn’t easy to put into words his impact when he passed. Here is what I said:

“Yes, I learned accounting in his classroom, but he also taught me more, including an appreciation of a broad diversity of great music. He was a multidimensional mentor, and his lessons stayed with me. He changed my life.” (Baruch Alumni Magazine)

Baruch Alumni Magazine: In Memoriam: Tribute to Seymour Eisenman (MBA ’69, PhD ’75)

Professor A. Wayne Corcoran – Baruch College

The most challenging course I took during my college years, including the MBA program, was the course in Cost Accounting taught by Professor A. Wayne Corcoran. 

On the first day of the class, I was one of 23 students there when he started by proceeding to solve a complex differential equation he had written on the board. There were only 18 in the next class, and we continued dwindling to the end of the semester. 

The preface of the textbook Professor Corcoran had authored began: 

“As much as anything, this is a book on philosophy.”

He went on to say:

“There is examination of conflicting theories and frank expression of opinion. Where my opinions are too outrageous, too out of keeping with established thought, or too naive, I have asked a colleague who is a supporter of a more prevalent or a more informed view in the particular area to expound on the opposing theory, so that you may better understand the controversy and form your own opinion.”

The course was an adventure in mathematics, operations research, philosophy, and many other mind-blowing topics.

He wanted us to think deeply about our work.

That textbook is still on my shelf, and I still pull it out occasionally.

My great Baruch accounting professors like Abe Briloff, Seymour Eisenman, and Wayne Corcoran were my first introduction to the knights in the battle for accounting integrity.

The Closest I Got To An Accounting Scandal

Several years after graduating, I got an excellent first-person view of accounting misinformation while serving a client.

Nay, let’s call it what it was, blatant accounting manipulation.

They were making the company’s performance look better than it was to keep the stock valuation inflated.

Here is what happened.

This client was a fast-growing, high-tech company that had just hired a new President and was looking for faster growth through a strategic transformation. 

We started working with him and the company, focusing on product management, engineering, design, and building. 

Our work progressed quickly and so well that he offered me a lucrative role to join the company as an officer driving transformation.

We had yet to dig into the revenue side of the business. This area was not a priority as the company reported quarterly sales and income increases, beating the street expectations each quarter. 

This business was an actual growth stock. On paper, anyway.

Before joining the company, I asked the President for some time to do my due diligence on the entire business. 

He agreed.

The company had several divisions, with the fast-growing European business led by the Global Head of Sales.

I spent some time with him and his staff and visited the European operations to see how they were scaling so quickly.

I saw customer-designated/owned inventory in the company warehouse for the first time. 

And the inventory was large and in every location. 

Curious about why customers, all large multi-national companies with owned storage locations, were buying products for deployment but leaving them in our client warehouse, I got evasive answers.

Shortly after that, with a few discreet accounting requests for data, we were able to put together a sketch of what was happening. 

We recognized the problem immediately.

Channel Stuffing Like This Is Financial Fraud

When sales were short of projections, the team would stuff the channel at the end of a quarter.

The selling business unit would make generous deals with friendly customers for orders sufficient to meet the shortfall. 

These customers would return the products soon after the quarter ended.

In the early design of this process, they shipped products to customers. 

But innovation soon took hold, and they figured out how to keep the products in the warehouse, saving the shipping and return costs but tracking them temporarily as customer-owned.

The end-of-quarter problem grew quarter by quarter as actual demand stayed flat.

When I met with the President to explain why I would not take the role, he learned for the first time about the problem.

He confronted the Chair and senior executives and, shortly after that, resigned from his role at the company.

The executive responsible for the manipulation became the new President.

Our team immediately disengaged from this client.

We were all subject to air-tight non-disclosures.

We moved on.

A few years later, a number of the company officers were charged with manipulation and securities fraud.

Understanding Accounting Misinformation – A Famous Accounting Fraud Case

Imagine a beautiful painting with a torn canvas beneath the surface. 

This structural tear is the reality of accounting misinformation, a facade of financial health hiding potentially deep problems. 

It’s a serious issue that can lead to catastrophic consequences for businesses and investors. 

Financial Crime by Enron

Remember Enron? 

Enron was one of the supposedly most innovative companies in the United States in the 1990s. Its reported financial health rested on massive accounting and corporate fraud. It eventually came to light, resulting in Enron declaring bankruptcy in December 2001.

Several of Enron’s senior executives faced criminal prosecution and were convicted.

A Senate committee staff report later found that the SEC, Wall Street analysts, and credit rating agencies had all failed to protect Enron’s investors.

Arthur Andersen was the accounting firm responsible for auditing Enron’s financial statements. 

Instead of acting as a knight using their shield to protect investors from potential manipulation, they turned it toward themselves. As an SEC investigation loomed, the firm told its staff to follow its document retention policy, and Enron audit documents were destroyed.

A jury found the firm guilty of obstruction of justice for destroying documents related to Enron’s audit, though the Supreme Court later reversed the conviction. By then, with its reputation severely damaged, Arthur Andersen had effectively dissolved its audit business.

Congress enacted the Sarbanes-Oxley Act to hold corporate executives more accountable for their company’s financial statements.

The law tightened the responsibilities of the Chief Executive and Chief Financial Officers, audit committees, and boards for accounting integrity.

The Enron/Andersen downfall is a stark reminder of the destructive power of accounting manipulation.

It also highlights the importance of genuine knights of honor in the fight against accounting manipulation. The few who worked on the dark side took down an entire firm with many honorable knights.

Traditional methods of detecting accounting fraud

For decades, complex accounting maneuvers have enabled corporations to bend the truth and manipulate profits and losses. 

Our knights in the fight have historically worked as diligent data detectives using several traditional methods to identify these shady practices. 

These methods included:

1. Financial Statement Analysis: Analyze financial statements to identify inconsistencies or irregularities within the accounts or across different periods.
2. Total Assets Comparison: Comparing the total assets of a company to industry benchmarks or previous periods can help identify discrepancies or potential fraud.
3. Management Review: Review financial statements and reports critically for suspicious or unusual transactions or patterns.
4. Tip Lines: Implementing anonymous tip lines or hotlines can encourage employees to report suspected fraud or misconduct.
5. Regression Analysis: Regression analysis involves analyzing relationships between variables to identify abnormal patterns or outliers.
6. Random Sampling: Traditional auditing techniques often use random-based selection methods to select a subset of transactions or records for closer examination.

It’s grueling, detailed, monotonous, and challenging work.

This gumshoe diligence is why there are so many jokes about accountants.

Before you go knocking accountants, remember that these people studied accounting or worked in it before they became famous:

1. Mick Jagger – studied finance and accounting at the London School of Economics
2. Robert Plant – briefly trained as a chartered accountant before becoming a musician
3. Bob Newhart – worked as an accountant before becoming a comedian
4. John Grisham – earned an accounting degree before becoming a lawyer and author
5. Kenny G – earned an accounting degree while playing professionally, before his solo career took off
6. John D. Rockefeller – worked as an assistant bookkeeper before becoming a businessman and philanthropist

Enough?

Data Science and Accounting Forensics – The Modern Knights

Technology and data analysis advancements have led to the development of more sophisticated methods for detecting accounting fraud.

Armed with advanced statistical tools, our modern knights in the fight are rooting out anomalies and revealing the hard facts behind the financial smokescreens.

Enter forensic accounting, also known as accounting forensics or financial forensics.

This specialty is a practice area within accounting that involves investigating financial reporting misconduct and financial crimes.

Forensic accountants combine accounting, auditing, and investigative skills, like the traditional knights, with data science, artificial intelligence, and other technologies to determine whether there has been any financial reporting misconduct.

An old idea updated – Benford’s Law

In 1938, the physicist Frank Benford published a paper called “The Law of Anomalous Numbers.” 

The principle was first noted by Simon Newcomb, a Canadian-American astronomer and mathematician, in 1881.

From this work emerged what came to be known as Benford’s Law, or the Newcomb-Benford law. 

Benford observed that in many real-world collections of numbers, small leading digits show up far more often than large ones. For example, if you look at many expense reports, you would find more expenses where the first digit is a 1 (e.g., $14.20) than the first digit is a 9 (e.g., $9.40).

It was genius.

In 1972, economist Hal Varian suggested that Benford’s Law could detect possible fraud in socio-economic data used for public planning. 

The reasoning rests on a reasonable guess, that people who make up numbers tend to spread their digits fairly evenly. So if you compare the first digits of their figures with the pattern Benford’s Law predicts, the made-up figures should stand out.

Benford’s Law Analysis Drives Increasing Internal Control

While running a business in the Middle East with many drivers, we became concerned about the possibility of expense manipulation when a simple tally of total gas reimbursements exceeded multiples of fuel requirements if the drivers operated non-stop.

While filling up my car at gas stations, I frequently observed drivers from other companies picking up discarded gas receipts.

Our CFO confirmed he was suspicious that the common disease of using discarded receipts as a valid expense might have infected our drivers. 

Curious to see how pervasive the problem was, we used a Benford’s Law analysis of a subset of expenses and discovered the problem extended well beyond gas receipts.

Implementing a zero-tolerance approach to expense falsification would have led to the removal of a large chunk of foreign staff, all of whom would be deported back home to certain financial peril.

We recommended a salary increase instead, and our generous Chairman approved it. We announced it along with a new zero-tolerance policy for expense fraud.

A higher integrity culture set in, and suspicious expenses declined dramatically.

Benford’s law unleashed

Today, we can see Benford’s law at work in a wide variety of real-world applications:

1. In Financial Auditing, to detect potential fraud or errors in financial statements and other financial data. If the leading digits of specific numbers deviate significantly from the expected distribution according to Benford’s Law, it may indicate fraudulent or manipulated data.
2. In Forensic Accounting, to analyze financial data in legal investigations. By examining the leading digits of transaction amounts, forensic accountants can identify suspicious patterns or irregularities that may require further investigation.
3. In election analysis, some researchers have tested vote counts against Benford’s law. But there is no scientific consensus that it works on elections, and the law has been misapplied to claim election fraud. Walter Mebane, a political scientist and statistician at the University of Michigan, put it plainly in 2020: “It is widely understood that the first digits of precinct vote counts are not useful for trying to diagnose election frauds.”
4. In Scientific Data Analysis, to help spot made-up figures in research data.

Benford’s law’s ability to identify irregularities and anomalies in numerical data makes it a valuable tool for auditors in data analysis and fraud detection.

The modern accounting Knight, armed with data science tools and technology, uses Benford’s law to reduce the success rate of accounting fraud.

The Beneish M-Score

Professor Messod Daniel Beneish – Indiana University, Kelley School of Business

Professor Messod Daniel Beneish joined Indiana University in 1996, long after I had graduated. 

His faculty profile lists his research interests as “corporate governance, earnings management, fraud detection, and insider trading.”

In 1999, he published a paper titled “The Detection of Earnings Manipulation” in the Financial Analysts Journal.

He hypothesized that companies with high sales growth, deteriorating gross margins, rising operating expenses, and rising leverage have greater incentives to manipulate profits. He expected them to do it by booking sales early, deferring costs, raising accruals, and slowing depreciation, though in his tests the depreciation, expense and leverage measures did not turn out to be significant.

The Beneish M-Score – Applied To Corporate Financial Statements

He introduced a model called the M-Score (the M stands for manipulation). 

The M-Score blends eight ratios built from a company’s financial statements into a single score. The higher the score, the higher the estimated chance that the company is manipulating its earnings. 

In his 1999 paper, the M-Score correctly identified 58 to 76 percent of manipulators in the sample he built it on, while wrongly flagging 7.6 to 17.5 percent of non-manipulators. On a fresh sample, it caught about half of the manipulators before the public found out.

That was a huge step forward in identifying manipulation.

A Cornell University Johnson Graduate School of Management student team used the M-Score, with a looser cutoff than the one Beneish published the following year (more on that in the model details near the end), to identify potential problems with Enron in 1998, three years before it fell, but the team discounted the clue.

From their report: 

“The 8-variable Beneish model shows that that Enron may be manipulating its earnings. We get a M-score of -1.89 for Enron, which is greater than the standard M-score of -2.22 used to gauge the likelihood of manipulation. The most significant factor contributing to the M-score manipulation statistic is the SGI. After close examination we were not concerned by the fact they were growing too fast because the sales increase comes from the recent acquisition of PGE. Additionally, the GMI shows deteriorating margins, the AQI shows increasing amounts of ‘soft’ assets, DEPI shows depreciation expense slowing down, and LVGI indicate rapidly increasing leverage. However, further examination of these indicators showed no cause for concern.” (Cornell student report)

They were on to something. They put the biggest driver of the high score, sales growth, down to Enron’s recent acquisition of PGE, a reason Beneish himself lists as a possible innocent cause. I suspect the prevailing positive bias towards Enron made that explanation easier to accept.

They did recommend selling the stock over the next nine months, though, because they saw little upside, in a year when Fortune was still naming Enron America’s most innovative company.

Corporate Fraud At the Economy Level

In 2019, Marketplace reports, Professor Beneish took the M-Score from single companies to the economy as a whole. 

He says, “This aggregate M-Score predicts recessions five to eight quarters ahead and is significantly associated with lower future growth in real GDP, real investment, consumption, and industrial production.”

In 2023, he and his co-authors David B. Farber of Indiana University and Matthew Glendening and Kenneth W. Shaw of the University of Missouri published their paper “Aggregate Financial Misreporting and the Predictability of U.S. Recessions and GDP Growth” in The Accounting Review.

Marketplace reported that earlier in 2023 the aggregate M-Score reached a 40-year high.

Here is what Matthew Glendening told Marketplace about this ominous sign: “Accounting manipulation matters for the economy at large.” Companies rely on other businesses’ reported earnings when they decide on hiring, purchasing, and production. Glendening added, “What we are finding is that the level of aggregate misreporting is very similar to what we’ve observed in pre-recessionary periods.”

The Beneish M-Score is a trusty sword our knights use to detect earnings manipulation. 

However, like any sword, it’s not infallible. 

After all, Beneish expects there to be false positives. The point is that when risks are high, caution is reasonable. The M-Score helps us become more cautious.

Warren Buffett once wrote, “It’s only when the tide goes out that you learn who’s been swimming naked.” 

The Beneish M-Score can miss many of those ‘swimming naked’ in the financial tide.

The Emergence of AI to Prevent Accounting Fraud

In my view, Artificial Intelligence (AI) is already pivotal in combating accounting fraud. 

It’s like a vigilant sentinel, tirelessly scanning vast amounts of financial data to detect anomalies that could indicate fraudulent activities. 

AI algorithms, particularly those based on machine learning, can learn from historical data, identify fraudulent behavior patterns, and then apply this knowledge to detect similar patterns in new data. 

This ability to learn and adapt makes AI a potent tool in the fight against accounting fraud.

In my experience, many financial institutions already use AI and machine learning to screen large volumes of transactions for anomalies and possible fraud.

In the future, we expect a more significant role of AI in combating accounting fraud. 

For instance, AI will analyze numerical data and the text in financial reports and communications, using natural language processing to detect subtle hints of fraud. 

Furthermore, AI could be integrated with blockchain technology to create immutable and transparent financial records, making it even harder for fraudsters to manipulate data. 

Limiting the Performance Incentives for Mismanagement

In my experience, one of the most significant drivers of accounting fraud is the pressure to meet short-term performance metrics.

Whether to build a reputation as a high-performer or to achieve results that deliver promotions, bonuses, higher stock-option value, or other compensation, the root cause is the same.

Poorly defined performance metrics leave much room for decisions outside the enterprise’s best interest or manipulation.

Salespeople with pricing responsibility, incented to increase sales, may do so without regard to new business profitability. 

Inventory managers tasked with reducing the value held may do so without regard to lost sales.

The simple reality is that poorly thought-out performance measures that affect people’s earnings create incentives for bad decisions.

Simply changing the performance metrics or balancing decisions between multiple individuals led to better choices and reduced opportunities for manipulation. 

Separating pricing from selling helped. Similarly, creating two metrics, sales and a profitability measure, for the executive brings balance to the decision-making.

An inventory turns metric combined with a service level or lost sales metric brings balance.

Professor Robert S. Kaplan – Harvard University

Professor Robert S. Kaplan is the Marvin Bower Professor of Leadership Development, Emeritus, at Harvard Business School. 

He joined the faculty there in 1984.

In 1988 he and his co-author Robin Cooper began publishing influential Harvard Business Review articles on the Activity Based Costing (ABC) concept and methodology. They helped popularize ABC in practice.

I remember using some components of this approach to recast the economics of client operations in the 80s. They were usually surprised to learn that where they were making money differed from what they originally thought. Their scaling assumptions, volume-based pricing, and many other management decisions changed to reshape performance fundamentally.

In the ’90s, he and his co-author David Norton pioneered and published “The Balanced Scorecard” in Harvard Business Review.

There are a few key reasons why I have always loved balanced scorecards and why many organizations rely on them:

• They provide a more comprehensive view of performance beyond just financial metrics. Focusing solely on financials can promote short-term thinking. The balanced scorecard helps managers see leading indicators across other perspectives (customers, processes, people) that drive future financial performance.
• They align activities with strategy. The scorecard makes the connection between longer-term strategic goals and day-to-day actions explicit. This linkage enables better strategic follow-through.
• They communicate the strategy across the organization. The scorecard methodology helps executives communicate strategic priorities and show how different units and teams contribute.
• They enable the measurement of intangible assets. Intellectual capital, brand, culture, and employee capabilities are intangible but vital to strategic success. The balanced scorecard provides a framework for tracking performance in these areas.
• They facilitate organizational change. Developing balanced scorecards requires cross-functional coordination and helps organizations identify and overcome barriers to change.
• They provide leading indicators. By monitoring non-financial metrics, managers can gain insight into future opportunities or problems before fully materializing in the financial statements.

Balanced scorecards are genius and critical to limiting short-term manipulative thinking.

A word of caution – the long fight

The forces on the other side are enormous, powerful, innovative, well funded, and have access to many of the tools our knights are using. 

Our regulators are slow. And when they make changes, there are often unintended consequences not considered.

Even when our tools work as expected, in my experience a strong bias in favor of the companies keeps many detectives from going public with their findings.

We need more Abe Briloffs. 

He wasn’t afraid to publish.

We need more Eisenman, Corcoran, Benford, Beneish, Kaplan, and other heroes (you won’t like this article’s size if I covered them all).

The coming battle for accounting integrity

As we stand on the brink of this new era, the future of artificial intelligence and data science in accounting shines brightly. 

It holds out the hope of a world where accounting misinformation is far rarer, and where the integrity of financial information is as solid as a diamond. 

But, like any journey, there will be challenges. 

In the hands of our accounting knights, AI and data science can be decisive allies in the fight against accounting misinformation. 

They are a beacon of hope, revealing the path toward a future of integrity and transparency in accounting. 

So, here’s to our knights and wizards, accountants, and data scientists, working together to safeguard the world of finance.

The world owes you its gratitude for your work!

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Details of The Beneish M-Score model

The following are the eight ratios and how they relate to the possibility of manipulation:

1. Days Sales in Receivables Index (DSRI): A significant increase in receivable days might suggest accelerated revenue recognition to inflate profits.
2. Gross Margin Index (GMI): A deteriorating gross margin is a worrisome indicator of a firm’s prospects and creates an incentive to inflate profits.
3. Asset Quality Index (AQI): If long-term assets other than property, plant and equipment grow as a share of total assets (for example, because the company capitalizes costs), the company may be pushing more of its costs into future years to lift today’s profits.
4. Sales Growth Index (SGI): Fast growth does not prove manipulation. But Beneish points out that professionals treat fast growers as higher fraud risks, because the need to keep raising money pushes managers to hit their numbers.
5. Depreciation Index (DEPI): If depreciation falls relative to net fixed assets, the company may have stretched its estimate of how long its assets will last, or switched to a method that raises income.
6. Sales, General, and Administrative Expenses Index (SGAI): If SG&A grows much faster than sales, analysts may read it as a bad sign for the company’s future, so Beneish expected a rising SGAI to go with a higher chance of manipulation.
7. Leverage Index (LVGI): Leverage is total debt relative to total assets. Beneish included it to capture the pressure to meet debt covenants (the conditions lenders attach to loans), though he notes that pressure may be too weak on its own to drive manipulation.
8. Total Accruals to Total Assets (TATA): TATA is total accruals (the change in working capital accounts other than cash, minus depreciation) divided by total assets. Researchers have used accruals to gauge how far managers use accounting choices to shape earnings. Higher accruals mean less cash behind reported profits, which Beneish linked to a higher chance of manipulation.

You calculate the M-Score using the eight variables according to the following formula:

Beneish M-Score = -4.84 + 0.92*DSRI + 0.528*GMI + 0.404*AQI + 0.892*SGI + 0.115*DEPI – 0.172*SGAI + 4.679*TATA – 0.327*LVGI

Beneish concluded that, for investors, a company scoring greater than -1.78 (i.e., a less negative or positive number) should be flagged as a potential manipulator and looked at more closely. The Cornell students, writing in 1998 before Beneish’s paper was published, used a lower cutoff of -2.22, which their report calls the standard. At Beneish’s -1.78, the -1.89 they computed for Enron in 1998 would have fallen just short of a flag.

Indiana University has a free version of the Beneish M-Score Calculator on its website.

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FAQs

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For Video Lovers – The Enron Story

Sources

Note: I have embedded sources throughout the document as I find them easier to use. If I have missed any, please drop me a comment, and I will fix it.

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A Note on General Misinformation and Manipulation

I have noticed a common theme in much of my writing.

It may be a reflection of my career in transformation. 

In several cases, managers misinterpret readily available data about a company’s prospects. In others, misguided managers manipulate the data. 

The results? Missed opportunities or, in some cases, dire performance requiring a turn-around.

Data manipulation is easy. 

The meaning of data, even solid data, can be manipulated to misinform.

Our government, the media, and others we should trust have all contributed to the epidemic of misinformation and manipulation.

It’s everywhere in daily life.

In my view, many medical advertisements are designed to steer you into influencing your doctor, who has far more information than you do about how well a treatment works.

Purdue Pharma won FDA approval for a label suggesting OxyContin was less prone to abuse, and an affiliate later pled guilty to claiming it was less addictive than other opioids. They misled talented, well-meaning doctors.

Food products advertised to sound healthier for you. Food isn’t healthy. People are, or not. A food product can be nutritious.

Some celebrities and famous politicians hawk products that they have never tried. Or for which there is little scientific support for their effectiveness.

What about politics? The environment? Culture wars?

I can go on, but you get the gist.

I like tackling misinformation and manipulation on these pages by discussing topical examples. 

I will always recommend data-based rather than opinion-based work.

Whenever available, I will go to credible data sources and illustrate the case as simply and neutrally as possible. 

The rest is up to you.

Please read some of my articles listed below and limit the impact of misinformation and manipulation in your lives.

• The Truth About How Media Is Misleading With Facts
• Combating Coronavirus Misinformation
• We must have a conversation about policing and racism in America
• Can You Differentiate Smart From Nonsense?
• Is a College degree really worth it?
• Why Are Facts Irrelevant To Online Analysts?
• Why You Will Lose Money If You Follow That Analyst
• Down With Doom
• Do You See What You Believe?