"Good question!  I'll ask you to hold that thought pending subtopic #3."  Keltham gestures back to his previous list of subtopics he shouldn't forget to talk about.  "Or actually I should maybe just write that one down..."

#1 - 'Published-experimental-reports' usually don't assign 'priors' or calculate 'posteriors', they just report all cheap details of the raw data, and maybe calculate some 'likelihoods' from obvious hypotheses

#2 - Separate experiments are usually supposed to avert 'conditional-dependencies', watch out for when that isn't true

#3 - If every obvious hypothesis has unexpectedly low 'likelihood' over all the combined data, it means the true theory wasn't in your starting set, often that different experiments had different hidden conditions

#4 - How no specially process the special meta-hypothesis 'all-other-hypotheses'

"Oh, and if that last Baseline word isn't translating, maybe the Taldane equivalent would be - everything we haven't thought of explicitly, all the theories we're not considering?"

"Anyways, this has hopefully ended up making #1 a little clearer."

"Going back to #2 - suppose that we tried summarizing the hypotheses here into three buckets.  One bucket that the propensity is less than 0.1 or 0.2 or 0.4, one bucket that the propensity is 0.5, one bucket that the propensity is 0.6 or 0.8 or 0.9."

"Is it then possible to describe the likelihood of our data NO YES YES NO NO, conditional on the first or third bucket?  For the middle bucket it's obviously 3125/100000 or 1/32."

"Good question!  I'll ask you to hold that thought pending subtopic #3."  Keltham gestures back to his previous list of subtopics he shouldn't forget to talk about.  "Or actually I should maybe just write that one down..."

#1 - 'Published-experimental-reports' usually don't assign 'priors' or calculate 'posteriors', they just report all cheap details of the raw data, and maybe calculate some 'likelihoods' from obvious hypotheses

#2 - Separate experiments are usually supposed to avert 'conditional-dependencies', watch out for when that isn't true

#3 - If every obvious hypothesis has unexpectedly low 'likelihood' over all the combined data, it means the true theory wasn't in your starting set, often that different experiments had different hidden conditions

#4 - How no specially process the special meta-hypothesis 'all-other-hypotheses'

"Oh, and if that last Baseline word isn't translating, maybe the Taldane equivalent would be - everything we haven't thought of explicitly, all the theories we're not considering?"

"Anyways, this has hopefully ended up making #1 a little clearer."

"Going back to #2... what would be a good entrance point there..."

"All right, so this isn't getting to #2 right away, just introducing an idea we'll use there."

"Suppose that we tried summarizing the hypotheses here into three buckets.  One bucket that the propensity is less than 0.1 or 0.2 or 0.4, one bucket that the propensity is 0.5, one bucket that the propensity is 0.6 or 0.8 or 0.9."

"Is it then possible to describe the likelihood of our data NO YES YES NO NO, conditional on the first or third bucket?  For the middle bucket it's obviously 3125/100000 or 1/32."

"Good question!  I'll ask you to hold that thought pending subtopic #3."  Keltham gestures back to his previous list of subtopics he shouldn't forget to talk about.  "Or actually I should maybe just write that one down..."

#1 - 'Published-experimental-reports' usually don't assign 'priors' or calculate 'posteriors', they just report all cheap details of the raw data, and maybe calculate some 'likelihoods' from obvious hypotheses

#2 - Separate experiments are usually supposed to avert 'conditional-dependencies', watch out for when that isn't true

#3 - If every obvious hypothesis has unexpectedly low 'likelihood' over all the combined data, it means the true theory wasn't in your starting set, often that different experiments had different hidden conditions

#4 - How to specially process the special meta-hypothesis 'all-other-hypotheses'

"Oh, and if that last Baseline word isn't translating, maybe the Taldane equivalent would be - everything we haven't thought of explicitly, all the theories we're not considering?"

"Anyways, this has hopefully ended up making #1 a little clearer."

"Going back to #2... what would be a good entrance point there..."

"All right, so this isn't getting to #2 right away, just introducing an idea we'll use there."

"Suppose that we tried summarizing the hypotheses here into three buckets.  One bucket that the propensity is less than 0.1 or 0.2 or 0.4, one bucket that the propensity is 0.5, one bucket that the propensity is 0.6 or 0.8 or 0.9."

"Is it then possible to describe the likelihood of our data NO YES YES NO NO, conditional on the first or third bucket?  For the middle bucket it's obviously 3125/100000 or 1/32."

"Good question!  I'll ask you to hold that thought pending subtopic #3."  Keltham gestures back to his previous list of subtopics he shouldn't forget to talk about.  "Or actually I should maybe just write that one down..."

#1 - 'Published-experimental-reports' usually don't assign 'priors' or calculate 'posteriors', they just report all cheap details of the raw data, and maybe calculate some 'likelihoods' from obvious hypotheses

#2 - Separate experiments are usually supposed to avert 'conditional-dependencies', watch out for when that isn't true

#3 - If every obvious hypothesis has unexpectedly low 'likelihood' over all the combined data, it means the true theory wasn't in your starting set, often that different experiments had different hidden conditions

#4 - How to specially process the special meta-hypothesis 'all-other-hypotheses'

"Oh, and if that last Baseline word isn't translating, maybe the Taldane equivalent would be - everything we haven't thought of explicitly, all the theories we're not considering?"

"Anyways, this has hopefully ended up making #1 a little clearer."

"Going back to #2... what would be a good entrance point there..."

"All right, so this isn't getting to #2 right away, just introducing an idea we'll use there."

"Suppose that we tried summarizing the hypotheses here into three buckets.  One bucket that the propensity is 0.1 or 0.2 or 0.4, one bucket that the propensity is 0.5, one bucket that the propensity is 0.6 or 0.8 or 0.9."

"Is it then possible to describe the likelihood of our data NO YES YES NO NO, conditional on the first or third bucket?  For the middle bucket it's obviously 3125/100000 or 1/32."

"Good question!  I'll ask you to hold that thought pending subtopic #3."  Keltham gestures back to his previous list of subtopics he shouldn't forget to talk about.  "Or actually I should maybe just write that one down..."

#1 - 'Published-experimental-reports' usually don't assign 'priors' or calculate 'posteriors', they just report all cheap details of the raw data, and maybe calculate some 'likelihoods' from obvious hypotheses

#2 - Separate experiments are usually supposed to avert 'conditional-dependencies', watch out for when that isn't true

#3 - If every obvious hypothesis has unexpectedly low 'likelihood' over all the combined data, it means the true theory wasn't in your starting set, often that different experiments had different hidden conditions

#4 - How to specially process the special meta-hypothesis 'all-other-hypotheses'

"Oh, and if that last Baseline word isn't translating, maybe the Taldane equivalent would be - everything we haven't thought of explicitly, all the theories we're not considering?"

"Anyways, this has hopefully ended up making #1 a little clearer."

"Going back to #2... what would make a good entrance point..."

"All right, so this isn't addressing #2 right away, just introducing an idea we'll use there, but."

"Suppose that we tried summarizing the hypotheses here into three buckets.  One bucket that the propensity is 0.1 or 0.2 or 0.4, one bucket that the propensity is 0.5, one bucket that the propensity is 0.6 or 0.8 or 0.9."

"Is it then possible to describe the likelihood of our data NO YES YES NO NO, conditional on the first or third bucket?  For the middle bucket it's obviously 3125/100000 or 1/32."

"Good question!  I'll ask you to hold that thought pending subtopic #3."  Keltham gestures back to his previous list of subtopics he shouldn't forget to talk about.  "Or actually I should maybe just write that one down..."

#1 - 'Published-experimental-reports' usually don't assign 'priors' or calculate 'posteriors', they just report all cheap details of the raw data, and maybe calculate some 'likelihoods' from obvious hypotheses

#2 - Separate experiments are usually supposed to avert 'conditional-dependencies', watch out for when that isn't true

#3 - If every obvious hypothesis has unexpectedly low 'likelihood' over all the combined data, it means the true theory wasn't in your starting set, often that different experiments had different hidden conditions

#4 - How to specially process the special meta-hypothesis 'all-other-hypotheses'

"Oh, and if that last Baseline word isn't translating, maybe the Taldane equivalent would be - everything we haven't thought of explicitly, all the theories we're not considering?"

"Anyways, this has hopefully ended up making #1 a little clearer."

"Going back to #2... what would make a good entrance point..."

"All right, so this isn't addressing #2 right away, just introducing an idea we'll use there, but."

"Suppose that we tried summarizing the hypotheses here into three buckets.  One bucket that the YES-propensity is 0.1 or 0.2 or 0.4, one bucket that the propensity is 0.5, one bucket that the propensity is 0.6 or 0.8 or 0.9."

"Is it then possible to describe the likelihood of our data NO YES YES NO NO, conditional on the first or third bucket?  For the middle bucket it's obviously 3125/100000 or 1/32."