The example I like to use is the confusion around COVID statistics, and how people mis-interpreted them.
For example, the rate of infections (or deaths) per day that was reported regularly in the news is actually: rate of infections * measurement accuracy * rate of measurement.
I.e.:
If more people turn up to be tested, the "rate" would go up.
If the PCR tests improved, the "rate" would go up.
A similar thing applies with hospitalisations and deaths. It might go up because a strain is more lethal than another strain, or because more people are infected with the same strain, or because more deaths are attributed to COVID instead of something else.
It doesn't help that different countries have different reporting standards, or that reporting standards changed over time due to the circumstances!
For example, the rate of infections (or deaths) per day that was reported regularly in the news is actually: rate of infections * measurement accuracy * rate of measurement.
I.e.:
If more people turn up to be tested, the "rate" would go up.
If the PCR tests improved, the "rate" would go up.
A similar thing applies with hospitalisations and deaths. It might go up because a strain is more lethal than another strain, or because more people are infected with the same strain, or because more deaths are attributed to COVID instead of something else.
It doesn't help that different countries have different reporting standards, or that reporting standards changed over time due to the circumstances!
Etc...
It's complicated!