The first thing to be investigated is integration, and this brings us to our first hypothesis: Is the data from the online store transmitted to us correctly? Sixty to seventy percent of problems are typically dealt with at this stage.

the further the peaks (averages) of these distributions are, the higher the power and the lower probability of a Type 2 error (that the null hypothesis will be accepted incorrectly). This is most logical, as the further the averages of the distributions are from each other, the more obvious the difference between the hypotheses becomes, thus making

A hypothesis is an idea for how to improve a product.

The purpose of analytics [10] is to help us formulate hypotheses. The purpose of statistics [10] is to test and confirm these hypotheses.

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

The go-to alternative for non-normal data is nonparametric tests.

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

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

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.