I think we tend to confuse ‘lack of strong statistical significance’ with ‘no predictive power’.

A small amount of evidence can substantially improve our decision-making...

… even if we cannot conclude that ’data with a correlation this large or larger would be very unlikely to be generated (p<0.05) if there were no correlation in the true population.

We, very reasonably, substantially update our beliefs and guide our decisions based on small amounts of data. See, e.g., the ‘Bayes rule’ chapter of Algorithms to Live By

I believe that for optimization problems and decision-making problems we should use a different approach both to design and to assessing results… relative to when we are trying to measure and test for scientific purposes.

We need to make a decision in one direction or another, and we need to consider costs and benefits of collecting and using these measures I believe we should be taking a Bayesian approach, updating our belief distribution,

… and considering the value of the information generated (in industry, the ‘lift’, ‘profit curve’ etc) in terms of how it improves our decision-making.

Note: I am exploring these ideas and hoping to learn, share and communicate more. Maybe others in this forum have more expertise in ‘reinforcement learning’ etc.

This is very reasonable; ‘no predictive power’ is a simplification.

Purely academically, I am sure a well-reasoned Bayesian approach would get us closer to the truth. But I think the conclusions drawn still make sense for three reasons.

I did not specify in the table, but the p-values for the insignificant coefficients were very high; often around p=0.85. I think this constitutes so little evidence that it would be too minor a Bayesian update to have to formally conduct.

Given that we do have evidence of some other variables being predictive, updating in favour of weighting those higher still makes sense (although maybe to a lesser degree than I implied in the post).

The time applicants and facilitators spend on the many different criteria we used is a cost (and a meaningful one for smaller groups). I would guess that cutting down the number of variables used would increase productivity more than what can be outweighed by the small updates we could make with little (but non-zero) predictive power.

I think we tend to confuse ‘lack of strong statistical significance’ with ‘no predictive power’.

A small amount of evidence can substantially improve our decision-making...

… even if we cannot conclude that ’data with a correlation this large or larger would be very unlikely to be generated (p<0.05) if there were no correlation in the true population.

We, very reasonably, substantially update our beliefs and guide our decisions based on small amounts of data. See, e.g., the ‘Bayes rule’ chapter of Algorithms to Live By

I believe that for optimization problems and decision-making problems we should use a different approach

both to design and to assessing results… relative to when we are trying to measure and test for scientific purposes.This relates to ‘reinforcement learning’ and to ‘exploration sampling’.

We need to make a decision in one direction or another, and we need to consider costs and benefits of collecting and using these measures I believe we should be taking a Bayesian approach, updating our belief distribution,

… and considering the value of the information generated (in industry, the ‘lift’, ‘profit curve’ etc) in terms of how it improves our decision-making.

Note: I am exploring these ideas and hoping to learn, share and communicate more. Maybe others in this forum have more expertise in ‘reinforcement learning’ etc.Thanks for writing this!

This is very reasonable; ‘no predictive power’ is a simplification.

Purely academically, I am sure a well-reasoned Bayesian approach would get us closer to the truth. But I think the conclusions drawn still make sense for three reasons.

I did not specify in the table, but the p-values for the insignificant coefficients were

veryhigh; often around p=0.85. I think this constitutes so little evidence that it would be too minor a Bayesian update to have to formally conduct.Given that we do have evidence of some other variables being predictive, updating in favour of weighting those higher still makes sense (although maybe to a lesser degree than I implied in the post).

The time applicants and facilitators spend on the many different criteria we used is a cost (and a meaningful one for smaller groups). I would guess that cutting down the number of variables used would increase productivity more than what can be outweighed by the small updates we could make with little (but non-zero) predictive power.