{"id":90050,"date":"2026-10-07T00:06:33","date_gmt":"2026-10-07T00:06:33","guid":{"rendered":"https:\/\/jimdgroup.com\/jd1\/the-logic-of-finding-problems\/"},"modified":"2026-10-07T00:06:33","modified_gmt":"2026-10-07T00:06:33","slug":"the-logic-of-finding-problems","status":"publish","type":"post","link":"https:\/\/jimdgroup.com\/jd1\/the-logic-of-finding-problems\/","title":{"rendered":"The Logic of Finding Problems"},"content":{"rendered":"\n

Originally published on Substack<\/a>, October 7, 2026.<\/em><\/p>\n\n\n\n

When people investigate an accident, a failed project, a scientific claim, a business collapse, or an alleged fraud, there is a natural temptation to look for the error\u2014the single mistake that supposedly explains everything.<\/p>\n\n\n\n

That way of thinking can be dangerous.<\/p>\n\n\n\n

There is no general rule of logic stating that a problem can contain only one error. A complicated event can contain dozens of errors, assumptions, omissions, misunderstandings, organizational failures, technical failures, and decisions that interact with one another. Sometimes one error causes another. Sometimes several independent errors converge to produce the same result. Sometimes one error remains hidden because another error attracts all of the attention.<\/p>\n\n\n\n

The Boeing 737 MAX disasters and the Theranos scandal illustrate this principle particularly well. They are very different events: one involved commercial aircraft and two fatal crashes; the other involved medical testing, investors, patients, and fraudulent representations. Yet both demonstrate why investigators must look beyond the first apparent problem.<\/p>\n\n\n\n

The central question should not merely be: \u201cWhat went wrong?\u201d<\/p>\n\n\n\n

It should also be: \u201cWhat else had to go wrong for this outcome to occur?\u201d<\/p>\n\n\n\n

And perhaps most importantly: \u201cWhat evidence establishes that we have found all of the significant problems?\u201d<\/p>\n\n\n\n

That last question is fundamental to serious problem-solving.<\/p>\n\n\n\n

A useful way to investigate almost any problem is to divide it into layers:<\/p>\n\n\n\n

Facts \u2192 Assumptions \u2192 Interpretation \u2192 Reasoning \u2192 Decisions \u2192 Outcome<\/p>\n\n\n\n

An error can occur at any level.<\/p>\n\n\n\n

For example, imagine an engineer receives an incorrect measurement. That creates:<\/p>\n\n\n\n

Incorrect measurement \u2192 incorrect assumption \u2192 incorrect calculation \u2192 incorrect design decision \u2192 system failure<\/p>\n\n\n\n

If the investigator discovers the incorrect measurement, it would be a mistake to conclude automatically, \u201cWe found the cause.\u201d<\/p>\n\n\n\n

The measurement may have been one cause, but the investigation should continue.<\/p>\n\n\n\n

Why was the measurement accepted? Why wasn\u2019t it independently verified? Was there a second measurement that contradicted it? Did the engineering process contain a verification requirement? Did someone know the measurement was questionable? Did the system contain safeguards that should have prevented the incorrect measurement from producing a catastrophic result?<\/p>\n\n\n\n

The deeper investigation may reveal several independent failures.<\/p>\n\n\n\n

Consider a simple argument:<\/p>\n\n\n\n

A is true.<\/p>\n\n\n\n

B follows from A.<\/p>\n\n\n\n

C follows from B.<\/p>\n\n\n\n

Therefore D is true.<\/p>\n\n\n\n

Suppose we discover that A is false.<\/p>\n\n\n\n

We have definitely found an error.<\/p>\n\n\n\n

But that does not tell us whether B follows from A, whether C follows from B, or whether the final conclusion is independently justified.<\/p>\n\n\n\n

There may be multiple errors:<\/p>\n\n\n\n

Error #1: A is false.<\/p>\n\n\n\n

Error #2: The connection between A and B is invalid.<\/p>\n\n\n\n

Error #3: B contains an additional false assumption.<\/p>\n\n\n\n

Error #4: The conclusion does not follow from C.<\/p>\n\n\n\n

Consequently, correcting Error #1 does not necessarily repair the entire argument.<\/p>\n\n\n\n

This distinction is enormously important in real-world investigations.<\/p>\n\n\n\n

The Boeing 737 MAX provides a particularly powerful example because official investigations examined multiple contributing factors surrounding the crashes of Lion Air Flight 610 in October 2018 and Ethiopian Airlines Flight 302 in March 2019.<\/p>\n\n\n\n

The aircraft\u2019s Maneuvering Characteristics Augmentation System, or MCAS, became a major focus of the investigations. The system could automatically command nose-down stabilizer movement based on angle-of-attack information.<\/p>\n\n\n\n

But the investigations did not simply reduce the entire disaster to \u201cMCAS was defective.\u201d<\/p>\n\n\n\n

The U.S. House Transportation and Infrastructure Committee\u2019s investigation identified numerous problems involving the design and certification of the aircraft. Among its findings, the committee reported that Boeing had failed to perform certain single- or multiple-failure analyses of MCAS after redesigning the system to increase its authority at lower speeds. The committee also documented concerns during development about faulty angle-of-attack information and MCAS behavior.<\/p>\n\n\n\n

The NTSB separately identified a fundamental problem in assumptions used during certification: investigators found a gap between assumptions about how pilots would respond to aircraft warnings and what crews actually encountered in the accidents.<\/p>\n\n\n\n

That illustrates the difference between a component failure and a system failure.<\/p>\n\n\n\n

The Boeing Error Tree<\/strong><\/h2>\n\n\n\n

737 MAX accidents<\/p>\n\n\n\n

\u251c\u2500\u2500 MCAS design<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 AOA input dependence<\/p>\n\n\n\n

\u2502 \u2514\u2500\u2500 System authority<\/p>\n\n\n\n

\u2502<\/p>\n\n\n\n

\u251c\u2500\u2500 Pilot\/system interaction<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Multiple alerts<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Workload<\/p>\n\n\n\n

\u2502 \u2514\u2500\u2500 Training<\/p>\n\n\n\n

\u2502<\/p>\n\n\n\n

\u2514\u2500\u2500 Certification process<\/p>\n\n\n\n

\u251c\u2500\u2500 Assumptions<\/p>\n\n\n\n

\u251c\u2500\u2500 Analysis<\/p>\n\n\n\n

\u2514\u2500\u2500 Oversight<\/p>\n\n\n\n

This is not saying that every item in the diagram had equal causal importance. Rather, it demonstrates how a complex failure can have multiple branches.<\/p>\n\n\n\n

The existence of one failure did not eliminate the possibility of other failures. Indeed, the system\u2019s danger arose partly from the interaction among multiple factors.<\/p>\n\n\n\n

Imagine that an investigator had examined the Lion Air crash and concluded:<\/p>\n\n\n\n

\u201cThe angle-of-attack sensor malfunctioned. Replace the sensor and the problem is solved.\u201d<\/p>\n\n\n\n

That would have identified an important fact, but it would not have been a complete investigation.<\/p>\n\n\n\n

The deeper questions would be:<\/p>\n\n\n\n

\u2022 Why could one erroneous sensor input have such authority?<\/p>\n\n\n\n

\u2022 Why did the software respond the way it did?<\/p>\n\n\n\n

\u2022 Why was the system designed that way?<\/p>\n\n\n\n

\u2022 What warnings did the pilots receive?<\/p>\n\n\n\n

\u2022 What did their training tell them?<\/p>\n\n\n\n

\u2022 What information did Boeing provide?<\/p>\n\n\n\n

\u2022 What information did regulators receive?<\/p>\n\n\n\n

\u2022 What assumptions were made during certification?<\/p>\n\n\n\n

\u2022 Were those assumptions tested against realistic failure scenarios?<\/p>\n\n\n\n

\u2022 What happened after the first accident?<\/p>\n\n\n\n

\u2022 Were similarities between the first and second accident recognized quickly enough?<\/p>\n\n\n\n

This demonstrates another important investigative principle:<\/p>\n\n\n\n

A first failure can be evidence of a larger system problem rather than the complete explanation for the problem.<\/strong><\/p>\n\n\n\n

Theranos presents an entirely different example.<\/p>\n\n\n\n

Theranos claimed that it had developed technology capable of performing a wide range of blood tests using very small samples. Evidence presented in the federal case showed that the technology had significant accuracy and reliability problems, that the system could not perform all of the tests claimed, and that conventional third-party analyzers were used for substantial portions of patient testing.<\/p>\n\n\n\n

The logical mistake would be to describe the Theranos collapse simply as:<\/p>\n\n\n\n

\u201cThe machine didn\u2019t work.\u201d<\/p>\n\n\n\n

That is incomplete.<\/p>\n\n\n\n

The failure involved technology, validation, representations to investors, representations to patients, business claims, regulatory issues, demonstrations, and internal knowledge.<\/p>\n\n\n\n

The federal case documented multiple categories of representations concerning the capabilities of the technology, projected revenue, Department of Defense relationships, pharmaceutical-company validation, and the Walgreens rollout.<\/p>\n\n\n\n

This creates another error tree.<\/p>\n\n\n\n

The Theranos Error Tree<\/strong><\/h2>\n\n\n\n

THERANOS FAILURE<\/p>\n\n\n\n

\u251c\u2500\u2500 Technology<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Accuracy<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Reliability<\/p>\n\n\n\n

\u2502 \u2514\u2500\u2500 Capability<\/p>\n\n\n\n

\u2502<\/p>\n\n\n\n

\u251c\u2500\u2500 Validation<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Testing<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Evidence<\/p>\n\n\n\n

\u2502 \u2514\u2500\u2500 Verification<\/p>\n\n\n\n

\u2502<\/p>\n\n\n\n

\u251c\u2500\u2500 Business<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Revenue forecasts<\/p>\n\n\n\n

\u2502 \u251c\u2500\u2500 Contracts<\/p>\n\n\n\n

\u2502 \u2514\u2500\u2500 Partnerships<\/p>\n\n\n\n

\u2502<\/p>\n\n\n\n

\u2514\u2500\u2500 Claims<\/p>\n\n\n\n

\u251c\u2500\u2500 Investors<\/p>\n\n\n\n

\u251c\u2500\u2500 Patients<\/p>\n\n\n\n

\u2514\u2500\u2500 Business partners<\/p>\n\n\n\n

Again, the point isn\u2019t that every item is identical or equally important.<\/p>\n\n\n\n

The point is that multiple failure mechanisms can coexist.<\/p>\n\n\n\n

Suppose an investigator had discovered:<\/p>\n\n\n\n

\u201cThe Theranos analyzer cannot perform all the tests claimed.\u201d<\/p>\n\n\n\n

That would be a major finding.<\/p>\n\n\n\n

But the next question should be:<\/p>\n\n\n\n

\u201cWhat happened after people knew or should have known this?\u201d<\/p>\n\n\n\n

Now the investigation changes. We have to examine:<\/p>\n\n\n\n

\u2022 What testing had actually been performed?<\/p>\n\n\n\n

\u2022 What validation existed?<\/p>\n\n\n\n

\u2022 What did employees know?<\/p>\n\n\n\n

\u2022 What did management know?<\/p>\n\n\n\n

\u2022 What was demonstrated to investors?<\/p>\n\n\n\n

\u2022 What was represented to patients?<\/p>\n\n\n\n

\u2022 What equipment was actually being used?<\/p>\n\n\n\n

\u2022 What did the company tell regulators?<\/p>\n\n\n\n

\u2022 What did it tell business partners?<\/p>\n\n\n\n

\u2022 What financial projections were being presented?<\/p>\n\n\n\n

\u2022 What happened when contradictory information appeared?<\/p>\n\n\n\n

The key lesson is that the investigation cannot logically stop at \u201cthe technology failed.\u201d The technology problem may be only one part of the larger causal structure.<\/p>\n\n\n\n

Another important logical distinction is that not every problem is the same kind of problem.<\/p>\n\n\n\n

There are at least several broad categories.<\/p>\n\n\n\n

Technical error: Something doesn\u2019t work as intended. For example, a sensor provides an incorrect measurement.<\/p>\n\n\n\n

Reasoning error: Someone draws an unjustified conclusion from available information. For example, \u201cThe sensor has never failed before, therefore it cannot fail.\u201d<\/p>\n\n\n\n

Information or disclosure failure: Important information isn\u2019t communicated to someone who needs it.<\/p>\n\n\n\n

Deliberate deception: A person knowingly communicates something false with the intention of inducing another person\u2019s reliance.<\/p>\n\n\n\n

These categories require different questions.<\/p>\n\n\n\n

In the Theranos case, the federal jury found Elizabeth Holmes guilty of investor-related conspiracy and three counts of wire fraud. The distinction matters because an investigation should not automatically classify every problem as a \u201cmistake.\u201d<\/p>\n\n\n\n

Sometimes there is a technical failure. Sometimes there is an organizational failure. Sometimes there is negligence. Sometimes there is a reasoning failure. And sometimes there is evidence of intentional deception.<\/p>\n\n\n\n

One of the most powerful tools for finding problems is to identify assumptions.<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat must be true for this conclusion to be correct?\u201d<\/p>\n\n\n\n

For example:<\/p>\n\n\n\n

\u201cThe system is safe.\u201d<\/p>\n\n\n\n

What must be true?<\/p>\n\n\n\n

\u2022 The hardware works.<\/p>\n\n\n\n

\u2022 The software works.<\/p>\n\n\n\n

\u2022 The sensors are reliable.<\/p>\n\n\n\n

\u2022 Failure modes have been considered.<\/p>\n\n\n\n

\u2022 Operators understand the system.<\/p>\n\n\n\n

\u2022 Operators respond as expected.<\/p>\n\n\n\n

\u2022 Training is adequate.<\/p>\n\n\n\n

\u2022 Documentation is accurate.<\/p>\n\n\n\n

\u2022 Maintenance is adequate.<\/p>\n\n\n\n

\u2022 Regulators have accurate information.<\/p>\n\n\n\n

\u2022 The system remains safe when components fail.<\/p>\n\n\n\n

Each assumption is another potential failure point.<\/p>\n\n\n\n

The same technique works with Theranos. If the claim is \u201cThe company has a revolutionary blood-testing technology,\u201d then the investigator must ask whether the technology exists, performs the claimed tests, has demonstrated accuracy and reproducibility, has independent validation, works outside controlled demonstrations, is actually used for patients, and satisfies applicable requirements.<\/p>\n\n\n\n

Once the assumptions are listed, investigation becomes much more systematic.<\/p>\n\n\n\n

Another powerful logical technique is the counterexample.<\/p>\n\n\n\n

Suppose someone says:<\/p>\n\n\n\n

\u201cThe system has never failed.\u201d<\/p>\n\n\n\n

You ask:<\/p>\n\n\n\n

\u201cCan you identify even one documented failure?\u201d<\/p>\n\n\n\n

One valid example disproves the universal claim.<\/p>\n\n\n\n

But there is an important asymmetry:<\/p>\n\n\n\n

One counterexample can disprove a universal claim. It cannot necessarily explain the entire problem.<\/p>\n\n\n\n

For example:<\/p>\n\n\n\n

\u201cWe found one inaccurate Theranos test.\u201d<\/p>\n\n\n\n

That proves that at least one inaccurate result occurred. It does not, by itself, establish how frequently errors occurred, why they occurred, whether they were systematic, whether management knew, whether other testing systems were involved, or whether the company\u2019s broader representations were accurate.<\/p>\n\n\n\n

Likewise:<\/p>\n\n\n\n

\u201cThe MCAS sensor was wrong.\u201d<\/p>\n\n\n\n

That establishes an important technical fact but does not, by itself, explain every aspect of the MAX disasters.<\/p>\n\n\n\n

A good investigator should deliberately attempt to disprove his or her own theory.<\/p>\n\n\n\n

Suppose the theory is:<\/p>\n\n\n\n

\u201cCause X produced the failure.\u201d<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat else could produce the same result?\u201d<\/p>\n\n\n\n

The investigator should construct competing hypotheses and compare the evidence supporting and contradicting each one.<\/p>\n\n\n\n

The objective isn\u2019t to find the theory you like best. The objective is to determine which explanations survive the evidence.<\/p>\n\n\n\n

Confirmation bias is particularly dangerous during investigations.<\/p>\n\n\n\n

Suppose an investigator believes:<\/p>\n\n\n\n

\u201cThe pilot caused the accident.\u201d<\/p>\n\n\n\n

Every pilot action can then be interpreted as supporting that theory.<\/p>\n\n\n\n

But the investigator should instead ask:<\/p>\n\n\n\n

\u201cWhat evidence would prove that theory incomplete or wrong?\u201d<\/p>\n\n\n\n

The same applies to corporations.<\/p>\n\n\n\n

Suppose an investigator believes:<\/p>\n\n\n\n

\u201cTheranos was simply a technology startup that failed.\u201d<\/p>\n\n\n\n

That hypothesis could cause the investigator to overlook evidence concerning representations made to investors and patients.<\/p>\n\n\n\n

Conversely, if the investigator begins with:<\/p>\n\n\n\n

\u201cEverything about Theranos was fraudulent from the beginning,\u201d<\/p>\n\n\n\n

the investigator may overlook distinctions between technical failures, business failures, individual actions, and the evidence required to establish particular claims.<\/p>\n\n\n\n

The best investigative method is therefore question-driven rather than conclusion-driven.<\/p>\n\n\n\n

One of the strongest questions an investigator can ask is:<\/p>\n\n\n\n

\u201cWhat evidence would cause me to abandon my current explanation?\u201d<\/p>\n\n\n\n

If the answer is \u201cNothing,\u201d then the investigation has probably become a search for confirmation rather than a search for truth.<\/p>\n\n\n\n

A strong hypothesis should expose itself to potential disproof.<\/p>\n\n\n\n

Another powerful method is to map dependencies.<\/p>\n\n\n\n

Suppose:<\/p>\n\n\n\n

A \u2192 B \u2192 C \u2192 D \u2192 E<\/p>\n\n\n\n

If A fails, E may fail.<\/p>\n\n\n\n

But suppose B independently contains another error.<\/p>\n\n\n\n

Then:<\/p>\n\n\n\n

A (error)<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

B (error)<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

C<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

D (error)<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

E<\/p>\n\n\n\n

There are now at least three errors.<\/p>\n\n\n\n

This is why simply fixing A may not fix E.<\/p>\n\n\n\n

Complex systems often behave this way.<\/p>\n\n\n\n

There are two particularly important types of multiple errors.<\/p>\n\n\n\n

Independent errors occur separately:<\/p>\n\n\n\n

Error A \u2192 Outcome<\/p>\n\n\n\n

Error B \u2192 Outcome<\/p>\n\n\n\n

Fixing A does not necessarily fix B.<\/p>\n\n\n\n

Cascading errors occur when one error creates another:<\/p>\n\n\n\n

Error A \u2192 Error B \u2192 Error C \u2192 Outcome<\/p>\n\n\n\n

The most dangerous situation may combine both:<\/p>\n\n\n\n

Error A<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

\u251c\u2500\u2500 Error B<\/p>\n\n\n\n

\u2514\u2500\u2500 Error C<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Outcome<\/p>\n\n\n\n

This is why complex investigations frequently cannot be reduced to a single cause.<\/p>\n\n\n\n

A simple but remarkably powerful investigative technique is repeatedly asking:<\/p>\n\n\n\n

\u201cWhy?\u201d<\/p>\n\n\n\n

For example:<\/p>\n\n\n\n

Why did the system fail?<\/p>\n\n\n\n

Because the control system behaved incorrectly.<\/p>\n\n\n\n

Why did it behave incorrectly?<\/p>\n\n\n\n

Because it received bad sensor information.<\/p>\n\n\n\n

Why could bad sensor information produce that behavior?<\/p>\n\n\n\n

Because the system design allowed it.<\/p>\n\n\n\n

Why was the design approved?<\/p>\n\n\n\n

Because the certification analysis treated certain failure conditions in a particular way.<\/p>\n\n\n\n

Why were those assumptions made?<\/p>\n\n\n\n

Because of assumptions concerning system behavior, pilot response, and certification requirements.<\/p>\n\n\n\n

Each \u201cwhy\u201d moves the investigation deeper.<\/p>\n\n\n\n

The same method can be applied to Theranos: Why did patients receive inaccurate results? Why was the system used? Why was the service presented as capable of doing what it did? Why did investors believe the claims? Each question exposes another layer that must be examined.<\/p>\n\n\n\n

Another important principle is:<\/p>\n\n\n\n

\u201cThe event closest in time to the outcome isn\u2019t necessarily the fundamental cause.\u201d<\/p>\n\n\n\n

The last event before an accident is often the easiest thing to identify.<\/p>\n\n\n\n

But a more complete chain might look like:<\/p>\n\n\n\n

Long-term decision<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Design decision<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Engineering assumption<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Implementation<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Warning<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Human response<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Immediate event<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Catastrophic outcome<\/p>\n\n\n\n

The immediate event may be only the final link.<\/p>\n\n\n\n

A complete investigation examines the entire chain.<\/p>\n\n\n\n

Once a failure is identified, ask:<\/p>\n\n\n\n

\u201cAt what earlier point could this problem have been detected or stopped?\u201d<\/p>\n\n\n\n

For Boeing, this question can be applied at design, testing, safety analysis, certification, pilot training, operational warnings, post-accident investigation, and corrective action.<\/p>\n\n\n\n

For Theranos, it can be applied to technical development, validation, internal testing, regulatory review, investor due diligence, laboratory operations, and patient testing.<\/p>\n\n\n\n

This produces a barrier analysis:<\/p>\n\n\n\n

Problem<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Barrier 1 \u2014 should detect it<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Barrier 2 \u2014 should stop it<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Barrier 3 \u2014 should contain it<\/p>\n\n\n\n

\u2193<\/p>\n\n\n\n

Outcome<\/p>\n\n\n\n

If the outcome occurs, ask why each barrier failed.<\/p>\n\n\n\n

The most important investigative question may be the simplest:<\/p>\n\n\n\n

\u201cWhat else?\u201d<\/p>\n\n\n\n

You find an error.<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat else is wrong?\u201d<\/p>\n\n\n\n

You find a false assumption.<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat else depends on that assumption?\u201d<\/p>\n\n\n\n

You find contradictory evidence.<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat else does that contradiction affect?\u201d<\/p>\n\n\n\n

You find a technical failure.<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat else allowed that technical failure to become consequential?\u201d<\/p>\n\n\n\n

You find a misleading statement.<\/p>\n\n\n\n

Ask:<\/p>\n\n\n\n

\u201cWhat other statements depended upon the same underlying claim?\u201d<\/p>\n\n\n\n

This mindset prevents premature closure.<\/p>\n\n\n\n

FACTS<\/strong><\/p>\n\n\n\n

1. What exactly happened?<\/p>\n\n\n\n

2. What evidence establishes it?<\/p>\n\n\n\n

3. Is the evidence reliable?<\/p>\n\n\n\n

4. Has it been independently verified?<\/p>\n\n\n\n

5. What evidence contradicts it?<\/p>\n\n\n\n

ASSUMPTIONS<\/strong><\/p>\n\n\n\n

6. What assumptions are being made?<\/p>\n\n\n\n

7. Which assumptions are documented?<\/p>\n\n\n\n

8. Which are merely inferred?<\/p>\n\n\n\n

9. What happens if each assumption is false?<\/p>\n\n\n\n

10. Are there hidden assumptions?<\/p>\n\n\n\n

LOGIC<\/strong><\/p>\n\n\n\n

11. Does the conclusion follow?<\/p>\n\n\n\n

12. Is there a non sequitur?<\/p>\n\n\n\n

13. Is there circular reasoning?<\/p>\n\n\n\n

14. Is correlation being confused with causation?<\/p>\n\n\n\n

15. Is possibility being confused with certainty?<\/p>\n\n\n\n

CAUSATION<\/strong><\/p>\n\n\n\n

16. What caused the immediate event?<\/p>\n\n\n\n

17. What caused that cause?<\/p>\n\n\n\n

18. What conditions allowed it?<\/p>\n\n\n\n

19. Were there multiple causes?<\/p>\n\n\n\n

20. Were any causes independent?<\/p>\n\n\n\n

ALTERNATIVES<\/strong><\/p>\n\n\n\n

21. What other explanations exist?<\/p>\n\n\n\n

22. What evidence supports them?<\/p>\n\n\n\n

23. What evidence contradicts them?<\/p>\n\n\n\n

24. What would disprove the preferred explanation?<\/p>\n\n\n\n

25. Has anyone actively attempted to disprove it?<\/p>\n\n\n\n

HUMAN FACTORS<\/strong><\/p>\n\n\n\n

26. What did people know?<\/p>\n\n\n\n

27. When did they know it?<\/p>\n\n\n\n

28. What did they believe?<\/p>\n\n\n\n

29. What information did they receive?<\/p>\n\n\n\n

30. What information did they not receive?<\/p>\n\n\n\n

ORGANIZATIONAL FACTORS<\/strong><\/p>\n\n\n\n

31. Were procedures followed?<\/p>\n\n\n\n

32. Were procedures adequate?<\/p>\n\n\n\n

33. Were warnings ignored?<\/p>\n\n\n\n

34. Were incentives creating pressure?<\/p>\n\n\n\n

35. Were responsibilities clearly assigned?<\/p>\n\n\n\n

VERIFICATION<\/strong><\/p>\n\n\n\n

36. Was the system independently tested?<\/p>\n\n\n\n

37. Was the information independently verified?<\/p>\n\n\n\n

38. Were assumptions stress-tested?<\/p>\n\n\n\n

39. Were failure scenarios examined?<\/p>\n\n\n\n

40. Were contradictory results investigated?<\/p>\n\n\n\n

COMPLETENESS<\/strong><\/p>\n\n\n\n

41. Have multiple errors been considered?<\/p>\n\n\n\n

42. Could one error have hidden another?<\/p>\n\n\n\n

43. Could errors have interacted?<\/p>\n\n\n\n

44. Could fixing one error leave another?<\/p>\n\n\n\n

45. What remains unexplained?<\/p>\n\n\n\n

FINAL CHALLENGE<\/strong><\/p>\n\n\n\n

46. What evidence would change the conclusion?<\/p>\n\n\n\n

47. What is the strongest argument against the current explanation?<\/p>\n\n\n\n

48. What alternative explanation best fits the evidence?<\/p>\n\n\n\n

49. What important information is still missing?<\/p>\n\n\n\n

50. What else could be wrong?<\/p>\n\n\n\n

The fundamental lesson is simple:<\/p>\n\n\n\n

Finding one error is not the same thing as finding the whole problem.<\/strong><\/p>\n\n\n\n

A serious investigation must distinguish between an error, a cause, a contributing factor, a failure in the reasoning process, and the complete explanation of an outcome.<\/p>\n\n\n\n

The Boeing 737 MAX investigations demonstrate how technical design, sensor information, software behavior, human factors, training, communication, and certification processes can interact. The Theranos case demonstrates how technology limitations, validation, representations to investors and patients, business claims, and management conduct can form a much larger problem than any single defective machine or inaccurate statement.<\/p>\n\n\n\n

Neither example supports the idea that there is a logical rule limiting a problem to one error.<\/p>\n\n\n\n

The opposite is often closer to reality:<\/p>\n\n\n\n

Complex failures should initially be presumed capable of having multiple contributing errors until the evidence establishes otherwise.<\/strong><\/p>\n\n\n\n

The investigator\u2019s job is therefore not merely to locate an error.<\/p>\n\n\n\n

It is to systematically search for every significant broken link in the chain.<\/p>\n\n\n\n

And after finding one, the most valuable question is often:<\/p>\n\n\n\n

\u201cWhat else?\u201d<\/p>\n\n\n\n

\u2022 U.S. House Committee on Transportation and Infrastructure, Boeing 737 MAX Final Committee Report (2020).<\/p>\n\n\n\n

\u2022 National Transportation Safety Board, findings concerning assumptions about pilot response and the certification of the 737 MAX.<\/p>\n\n\n\n

\u2022 Federal Aviation Administration, congressional testimony and materials concerning the 737 MAX grounding and certification.<\/p>\n\n\n\n

\u2022 U.S. Department of Justice, United States v. Elizabeth Holmes and related Theranos case materials.<\/p>\n\n\n\n

\n\n\n\n

This article first appeared on my Substack.<\/strong> Read it on Substack<\/a> to comment, share, or subscribe for new posts.<\/p>\n","protected":false},"excerpt":{"rendered":"

Why one error is rarely the whole problem.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_oxygen_hide_in_design_set":false,"_oxygen_tags":"","footnotes":""},"categories":[11],"tags":[],"class_list":["post-90050","post","type-post","status-publish","format-standard","hentry","category-tech_blog"],"yoast_head":"\nThe Logic of Finding Problems | JIMDgroup.com -Software Engineer<\/title>\n<meta name=\"description\" content=\"Why one error is rarely the whole problem: how the Boeing 737 MAX and Theranos failures show that serious investigations must keep asking "what else?"\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/drjimd.substack.com\/p\/the-logic-of-finding-problems\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" 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