Going to CFAR looks like it increases comfort and confidence with Goal-factoring. If we accept that goal-factoring is a useful thing, then by self-report CFAR helps.
what? We have P(comfortable | cfar)=11.2%<36.8%=P(comfortable | no cfar), and similarly for confident.
11.2% is if I remove the CFAR attendees. 36.8% is if I remove the non-attendees. Possibly this is a backwards way of setting things up but I think it’s right?
Say I have a general population and I know how many pushups they can do on average (call this Everyone Average), and I remove everyone who goes to the gym and see how many pushups those remaining can do on average (Call this Gym-Removed Average) and then I go back to the general population again this time removing everyone who doesn’t go to the gym (Call this No-Gym-Removed Average.)
This is a confusing label scheme but I don’t immediately know what the better one is.
If No-Gym-Removed Average < Gym-Removed Average, then it looks like the gym helps.
(Totally possible I’m screwing something up here still)
Ok first, when naming things I think you should do everything you can to not use double-negatives. So you should say “gym average” or “no gym average”. Its shorter, and much less confusing.
Second, I’m still confused. Translating what you said, we’d have “no gym removed average” → “gym average” (since you remove everyone who doesn’t go to the gym meaning the only people remaining go to the gym), and “gym removed average” → “no gym average” (since we’re removing everyone who goes to the gym meaning the only remaining people don’t go to the gym).
Therefore we have,
gym average = no gym removed average < gym removed average = no gym average
So it looks like the gym doesn’t help, since those who don’t go to the gym have a higher average number of pushups they can do than those who go to the gym.
what? We have P(comfortable | cfar)=11.2%<36.8%=P(comfortable | no cfar), and similarly for confident.
11.2% is if I remove the CFAR attendees. 36.8% is if I remove the non-attendees. Possibly this is a backwards way of setting things up but I think it’s right?
Say I have a general population and I know how many pushups they can do on average (call this Everyone Average), and I remove everyone who goes to the gym and see how many pushups those remaining can do on average (Call this Gym-Removed Average) and then I go back to the general population again this time removing everyone who doesn’t go to the gym (Call this No-Gym-Removed Average.)
This is a confusing label scheme but I don’t immediately know what the better one is.
If No-Gym-Removed Average < Gym-Removed Average, then it looks like the gym helps.
(Totally possible I’m screwing something up here still)
Ok first, when naming things I think you should do everything you can to not use double-negatives. So you should say “gym average” or “no gym average”. Its shorter, and much less confusing.
Second, I’m still confused. Translating what you said, we’d have “no gym removed average” → “gym average” (since you remove everyone who doesn’t go to the gym meaning the only people remaining go to the gym), and “gym removed average” → “no gym average” (since we’re removing everyone who goes to the gym meaning the only remaining people don’t go to the gym).
Therefore we have,
gym average = no gym removed average < gym removed average = no gym average
So it looks like the gym doesn’t help, since those who don’t go to the gym have a higher average number of pushups they can do than those who go to the gym.