Except that when the hypothesis space is large, people test hypotheses because they strongly updated in the direction of them being true, and seeing empirical data produces a later, weaker update. Where an example of ‘strongly updating’ could be going from 9,999,999:1 odds against a hypothesis to 99:1 odds, and an example of ‘weakly updating’ could be going from 99:1 odds against the hypothesis to 1:99. The former update requires about 20 bits of evidence, while the latter update requires about 10 bits of evidence.
Interesting point. I guess my intuitive notion of a “strong update” has to do with absolute probability mass allocation rather than bits of evidence (probability mass is what affects behavior?), but that’s probably not a disagreement worth hashing out.
Except that when the hypothesis space is large, people test hypotheses because they strongly updated in the direction of them being true, and seeing empirical data produces a later, weaker update. Where an example of ‘strongly updating’ could be going from 9,999,999:1 odds against a hypothesis to 99:1 odds, and an example of ‘weakly updating’ could be going from 99:1 odds against the hypothesis to 1:99. The former update requires about 20 bits of evidence, while the latter update requires about 10 bits of evidence.
Interesting point. I guess my intuitive notion of a “strong update” has to do with absolute probability mass allocation rather than bits of evidence (probability mass is what affects behavior?), but that’s probably not a disagreement worth hashing out.