In Bayes Theorem why do we say :given that" when "out of" is more understandable. (Why is Conditional on referred to as "Given") I understood the answer to my problem here when I substituted the "given that" symbol with the phrase "out of"
I got this idea from 3Blue1Brown where Grant points out that people are less confused when this phrase is used.
So why do we persist with using "given that"?
As a software developer I am used to using "given that" in pseudo code for unit tests, so the phrase especially threw me.
[Update]
I realise now that I am falling into the base-rate fallacy and am looking for an easy way to remember how to avoid it. I have often heard the "conditioning on" symbol read as "given that" and when that happens I get confused. For example in "“Given that it’s cloudy, the probability of rain is high”  when we write it as P(Rain|Cloudy) I tend to read this "P(Rain and Cloudy) given it is cloudy"
So I mistakenly think that we already know that it is cloudy (i.e we are given this) so we just need the probability of rain.
[Update]
I reverted to my original question with clarification in brackets. To hopefully make the question read better.
[Update]
Wikipedia mentions that

"the conditional probability of A given B" ...
can also be understood as the fraction of probability B that
intersects with A



As a software developer I am used to using "given that" in pseudo code for unit tests, so the phrase especially threw me.

First of all, different disciplines use different terminologies. That includes sometimes using same words differently.
But taking this aside, in BDD the tests are written in given-when-then, where

The given part describes the state of the world before you begin the behavior you're specifying in this scenario. You can think of it as the pre-conditions to the test.

Conditional probability is also about focusing (conditioning) on a specific scenario. “Given that it’s cloudy, the probability of rain is high” or “given that the ground is wet, there’s elevated probability that it rained.” Same as the test functions in a context, here we look at the probability of an event in a context.
