Status Updates From Weidenfeld Nicolson Everyth...

For bayesian each new result derive part of new prior for next bit of info. It also provides degrees of belief to range of possible realities unlike frequentist. Where bayesian leads to probability distribution—translate to things that could happen. Improving likelihood. Statistically significant result is just one obvious answer that raise the bar. Bayesian or frequentist depends on your decision making approach.

Getting more interesting now—since its moving in timeline. Scientist falsifying data, sampling vs inferential probability & bayes still ahead for measuring hypothesis. Bayesian creates avenue for scepticism & raising evidentiary bar. Historical data as guide for future data—drawing empiricism. Science advances by falsifying false hypothesis that some are easier to reject (to find the true hypothesis)

Bayes model that acknowledges subjective nature of probability that hides behind solid statistics. ofc at this stage bayes theorem are still developing. then rise if frequentism (which is opposite of bayesian). AND the racist nature of frequentist at that time (i.e., galton) how he came up with stereotypical theories about the races. fall of bayesianism where it received critics, currently still historical heavy

Really goes into bayes theorem. The intro highlighted its significance and how it relates to our daily lives and first part of chapter 1 highlights historical background of bayes (and relates scholar) and the development of bayes theorem—what goes wrong and what accelerates