What does it mean that binary categories lose information?

Dividing things into only two groups forces a false simplicity.

Example: Your team evaluates candidates as 'hire' or 'no hire.' One interviewer says no hire because the candidate lacked system design depth. Another says no hire because they seemed arrogant. A third says no hire because they want too much money. The binary output — no hire — hides three completely different signals. When the hiring manager reviews the pipeline, all they see is a row of rejections. They can't distinguish a compensation mismatch (easily fixable) from a skills gap (trainable) from a cultural concern (harder to change). The binary collapsed three dimensions of information into one bit.

Try this: Find a decision you recently made using binary framing — approved/rejected, good/bad, yes/no. Write down the actual factors that influenced your judgment. How many distinct dimensions did you compress into two buckets? Rewrite the decision using a scale (1-5 or 1-10) for each dimension. Notice what information reappears when you stop forcing a binary.