Separates “Assumptions” from “Hard Evidence” in gathering data

Assumptions are at the root of most mistakes, yet our thinking is riddled with
them. We have all experienced some frustration in communication because
someone assumes information—“assumed” that the arrangements would be the
same as last week, “assumed” that the time would be the same, or “assumed”
that everyone has understood, etc. While this kind of confusion is irritating and
frustrating, it is usually not as costly as making a decision or solving a problem
based on information or assumptions that have not been challenged!
We are rarely in possession of every piece of relevant information needed
to resolve issues. The temptation is strong to complete the gaps in our data
by filling these “data holes” with incomplete or speculative information based
on assumptions—sometimes quite reasonable assumptions, but assumptions
nevertheless.
The danger is that the pieces of data based on assumptions will be mistaken for
hard evidence if they are not sorted, classified, and clearly marked to remind us.
If we resolve the issue relying on the assumed evidence, we have probably
increased the risk considerably. That might be something we can live with
because of other factors, but we should at least be aware of the risks involved in
not thoroughly reviewing the evidence.
Some suggestions for enhancing performance in this area are as follows:
1. Use a visual method to display data as it is gathered. Mark assumptions or
loose and untested data (versus hard or intangible evidence) with different colored
stickers or some other way to graphically distinguish these two
categories of data.
2. Develop a checklist of criteria in order to verify whether or not the data can be
classified as “hard evidence.”
3. Work through the “assumptions” list to see if you can reclassify some
information as factual.
4. Take all pieces of data based on assumption or calculated guesses and mark
them with an “R” to clearly convey that an element of risk surrounds actions
based on this information.
5. Before arriving at a solution, check to see how dependent your decisions are
on assumptions and generalizations, as opposed to hard facts and evidence.
If they are too dependent upon the former, you may want to gather more hard
data.