Questions Are The Precursor Of Knowledge

The world is filled with data. It is meaningless until you know it is true when it can become information and then you can investigate to make it into knowledge.

A tweet from Dr Aaron Kheriaty @akheriaty provides an example. The image is from a CDC report. The link is shown but does not link as it is part of the picture.


This is data although it is possible it is true. CDC is not so reliable as it once was for purveying truth, but for now, let’s accept it.

What does it mean?

Meaning is the beginning of knowledge. Assigning meaning to information is a necessary skill and one often overlooked in the rush to judge.

Questions lead to meaning. So, let’s think of a few.:

Begin with an assessment of the information provided. Is the comparison to excess deaths at age 85 meaningful? Probably not very. The coronavirus seems relatively harmless in the absence of other problems with one’s health. The under 25 group was nearly immune in the early months and the deaths that appeared were generally connected with significant preexisting conditions. That there is a 4% excess death rate may not have any significant meaning as there are very few deaths in the group’s baseline. If normal is 1,000 per million, a 4% excess is 40 deaths in a million. It is very hard to attribute a cause to that.

The older groups may have some meaningful information included.

What are the sizes of the population of each age group? What is the expected death rate within each? That will help us to understand the relationships we need when looking further. At first glance, a 55% excess death rate seems formidable but as we saw with children if the numbers are small, the percentage doesn’t help with an answer. Never pay much attention to average increase when the baseline number is low. If something happens twice in a million and increases to three in a million, the change is +50%. Notice there is now way to get an increase lower than that.

Percentage change usually show us where to look first though.

Could I get a series of these particular graphics so I could assess the change over time?

  • Is the over 85 excess falling or rising? If falling, by August 2021, the beginning of the data set, the most vulnerable may have already died. That leads to a question of causation. Was it the virus or the heart condition? Did the person’s system become overloaded? The straw that broke the camel’s back.
  • Are the 25 to 44-year-old group results much higher than for early periods? If so when did the excess begin to appear? How fast did it grow?
  • For the 45 to 85 group what does the time sequence look like? Is there information on co-morbidities in each group?

Are there non-Covid causes of death that have changed dramatically? Suicide, overdoses, etc. As you refine data you find subgroups that have not changed, as well as several that have. Overall averaged results around causation mislead and are usually intended to mislead.

Are the results in the United States similar to the results in other countries? Especially the non-virus parts.

Where next?

I have no ability to get the data and only limited ability to analyze it. Data scientists would eventually come up with correlations, maybe leading to causes between several variables connected to but not the virus itself.

It is not smart to look for answers to what you think are the causes, but it is human nature. The best data scientists let the information lead them to an answer, not make their mind up and then look for proof of their thesis. We non-experts usually follow the weaker course.

Always pay attention to two factors.

  1. Correlation is not evidence of causation although it is a good place to start looking for causation.
  2. Cognitive bias is real. It is very hard to look for things when you think you know the answer.

What we find?

First of all, let’s choose to ignore two potential causes.

  1. The vaccine kills people and the time comparison may be correlated with the excess death problem.
  2. The lockdowns killed people and we may be able to see a time correlation.

Why ignore those for now? Because they will appear more vividly if they are causes and we have found little else. Their clarity will enhance as other things are discarded or assessed as contributing but of little effect.

Things to examine include contributing factors to death. We know that for people over 85, at least half have four or more co-morbidities. We should examine which they are. If heart disease or diabetes always are in the grouping we might like to keep track of that. Maybe those with a poor diet are always affected worse. Immobility is related? How about Vitamin D deficiency? Maybe some potentially contributing factors are caused by others. Do you suppose immobility increases the chances of low Vitamin D levels?

In the 25-44 age group There are likely fewer co-morbidities so there may be an easier analysis. Does anyone know the effect of inadequate sleep, stress from the changing work environment, and the change in the conflict level with both children and spouse?

The three groups between 45 and 85 are confusing. A more refined assessment might provide help. I think the averaging effects are the source of the confusion but more information would help.

Where the thought experiment leads me

Comparing the ends might offer the most help 25 to 45 and over 85 are vulnerable to different degrees but likely have some useful comparisons. Under 25 likely has too few clear indicators.

What we might discover. Emphasis on might.

  1. Healthy people can fight off the infection. Unhealthy people not so much. The degree of unhealthy matters most.
  2. Does the virus cause death or is it a catalyst that emphasizes other defects?
  3. The Great Barrington Declaration was more right than admitted early on. Universal lockdowns were not very valuable and caused other harm. Isolating the most vulnerable would have been adequate.
  4. Policy decisions were made poorly. If the virus was highly contagious as offered, then most people were going to get it eventually. Prevention was thus an improbable success vector. Why were treatment protocols not more fully explored? My preference is that in an emergency the first step is to stop the bleeding. There is no limit on what you can try so long as you believe the chosen action won’t do more harm. In the beginning, whether it will work or not is not a valid question. Wide experimentation and reporting results would find workable methods in a few weeks, at most a few months. If you know everyone will get it, doesn’t treatment become the priority?
  5. Vaccines seem not to have been the panacea promised. They might have been harmful to some people. It would be nice to know the important variables. As with every other medication known, there will be people where the use of the medication is contraindicated. The untested magic bullet approach should not be repeated or continued.
  6. Healthy people are harder to kill. Know what healthy means and get there. Keep in mind that health is a spectrum. Healthy is a power function. You get a large share of the benefit with relatively smaller inputs than you might expect. It is not necessary to run the Boston Marathon in under 2 hours and 20 minutes to be healthy. Health is an area that conforms to Edmund Burke’s assessment of action. “Nobody made a greater mistake than he who did nothing because he could only do a little.” I have seen an opinion that claims burning an extra 3500 calories a week is enough to stay in reasonable shape. It could be anything. Gardening an hour a day would hit 60% of that goal. Trying to reach Olympic athlete status will more likely lead to injury than good health.
  7. Learn about diet. Learn about vitamins and minerals.
  8. Understand the idea of risk assessment.
  9. Prepare. The world is not risk-free.

The bits to take away

Be healthier. It cannot hurt you and it has benefits beyond disease prevention.

There are too many people who are invested in the Covid response. Expect little transparency, and that will be reluctantly provided.

Question everything and avoid cognitive bias as best you can.

I build strategic, fact-based estate and income plans. The plans identify alternate ways to achieve spending and estate distribution goals. In the past, I have been a planner with a large insurance, employee benefits, and investment agency, a partner in a large international public accounting firm, CEO of a software start-up, a partner in an energy management system importer, and briefly in the restaurant business. I have appeared on more than 100 television shows on financial planning. I have presented to organizations as varied as the Canadian Bar Association, The Ontario Institute of Chartered Accountants, The Ontario Ministry of Agriculture and Food, and Banks – from CIBC to the Business Development Bank.

Be in touch at 705-927-4770 or by email at

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