ion to and that's what we control for so we have to do plausibility judgments when we're running our experiment okay now we're done and we have our data okay so now we have our data and we're going to interpret the data what is it to interpret the data it's to derive implications from the data how many implications can you derive from any set of propositions oh guess what it's combinatorially explosive so well which one should i derive well you're going to derive the ones that make the most sense are reasonable stand to good reason right right you know should be taken seriously you think people should pay attention to them so notice what i'm showing you before during and after scientific experimentation right you have to rely on plausibility judgments first of all does that make sense as a case to you yeah it makes sense because because of the potential infinity and because of both the the limited resources and the commentary explosive you need to be making those judgments along the way to keep things running in normal fashion i think could you could you just expand for a second more on the difference between the two uses of plausibility in terms of plausibility as probably as probabilistic and why that's why that's not a good metric yeah so let me put it this way and this came out with the discussion i've done with leo ferraro probability is our estimation of where we will find truth so if we mean probability we use plausibility to mean probability all we're doing is saying probability twice we mean it's just our estimation of where we'll find truth but there's another meaning of plausibility which is we should take it seriously we should pay attention all that and that's our estimation of where we expect to find probability so plausibility is our best way of estimating where our we're going to turn up evidence that will give us clarity about the probability and then we use probability right because of the fallible nature of science to make you know to make estimations of where truth is to be found so yes that s