10/05/2026
This information can apply to most any situation and not necessarily, science. The scripture says "my people suffer for a lack of knowledge."
“What you see in the dataset when you know the subject.” The key word in the above sentence is KNOW. Do you know the subject?
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Many tasks in science rely on structured data: information organized into set fields, similar to columns in a spreadsheet. The blueprint that defines these fields is called a schema. Everyone working on a task sees the same schema, but what you notice within it depends entirely on your background.
Here is how specialized knowledge makes a difference:
- Spotting impossible entries: Imagine a digital medical form where a field accepts any number. A physiological value that no human body could produce might pass automated validation without error, but a medical professional immediately recognizes that the data is incorrect.
- Evaluating field importance: Domain experts know which fields require strict formatting and which allow flexibility. In a health record, a dosage field requires precise numbers, whereas a general notes field can remain open-ended.
- Identifying structural flaws: Poorly designed schemas focus on what is easy to store, whereas well-designed schemas focus on what is meaningful. If a patient form only allows space for one symptom when clinical reality involves several, an expert can identify the design flaw and suggest a better structure.
So let's consider these same assertions and perceptions applied to human relationships.
When others see something out of order in someone; something that doesn't appear to "fit" within the schema, i.e. the dataset; their background on the subject and with the subject will influence what they see.
Unless they really KNOW the subject they will make a mistake in their assertions. Simply stated, people often make judgments based upon their own personal understanding of a matter. And typically, their assertions are predicated on perceptions they have developed about others based upon their own background.
We can apply this same reasoning to human relationships and information gained from some human relationships.
People often assess and determine outcomes based upon preset ideas and conclusions—based upon personal experience or perhaps a skewed perception of an individual or an individual’s reaction to a thing.
A reaction can be likened to that of a person's schema, their dataset for example. When you encounter something in the dataset that appears incorrect or amiss, your attempt to correct the information will fall short unless you really KNOW the subject.
Therefore, it's wise to step back to analyze something from different angles before making a rash judgment against anything that appears to be amiss in an individual’s schema/dataset.
It's not uncommon for an individual to misinterpret what they see when they are viewing the data from only one angle. They may not have the special skills to form a concrete opinion about the situation. Secondly, they may not know enough about the subject. The same can also be said for certain expectations we place on people. The same applies to assessing certain situations we may see in other people. For example, if you can bake a cake from scratch without needing to use teaspoons and cups for the ingredients, then you KNOW the recipe. If you improperly assess the formula for the ingredients then you’ll have a crummy tasting cake.
Sometimes something is placed into the ingredients causing a schism in the recipe of a relationship. Some issues in a relationship can be viewed as the result of either party having a skewed recipe that poorly affects the outcome of the relationship. When the ingredients used to form the relationship have changed, a schism occurs in the dataset. A specialist viewing the data can recognize the disturbance and adjust the formula to help improve and eventually administer the correct antidote.
This can be said for certain behaviors and situations we may witness in others. Sometimes, the ingredients in a person has been tampered with and depending on what was added or removed, what is produced can be very caustic. Under scrutiny, the specialist KNOWS why the subject is now caustic and knows how to fix the problem. So frankly speaking, it's problematic to assess a schema or a dataset when you don't know the subject as well as you think you do. Some assertions require specialization. You have to KNOW the subject. If you're not a specialist in terms of certain human matters then you should reserve your judgments before inserting your opinion. It's possible the conclusions you're drawing from your assertions about another person's dataset are dead wrong.
