Insight means the capacity to gain an understanding of the reasons for something occurring. This is precisely what analysts want to achieve.

nine out of ten hypotheses don’t pan out. But you have no idea that a hypothesis will not produce the desired result until you are well into the testing process. I believe that it is best to kill a hypothesis as early as possible – as soon as the first sign that the idea won’t take off presents itself.

All measurements contain errors. This is a fact, get over it. Errors themselves should be noted and not considered errors as such (I’ll explain how we can monitor this in a later chapter).

Kozyrkov implores us to “always evaluate decision quality based only on what was known at the time the decision was made.”

I believe it is better to use one-sided hypotheses. After testing an idea, we try to improve the metric. Here, we are interested in whether it has improved or not (Hypothesis H1).

Fisher statistics, the p-value is a universal number that it understandable to statisticians and allows them to reject the null hypothesis. The p-value was not a thing before Fisher

In his 1925 monograph Statistical Methods for Research Workers, Ronald Fisher (the founder of hypothesis testing) outlined concepts such as the statistical significance criterion, the rules for testing statistical hypotheses, analysis of variance, and experiment planning. This work defined our current approach to experiment planning.

nonparametric methods are better suited to small samples. There’s no point using nonparametric statistics if you’ve got a lot of data (for example n > 100).

Classical machine learning can be divided into three types: supervised learning unsupervised learning reinforcement learning.