Roman's Data Science: How to monetize your data

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

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.

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.

Evolutionary hypotheses, where one parameter is slightly optimized, have a less profound effect than revolutionary hypotheses, where the approach is fundamentally different. That said, evolutionary hypotheses are more likely to bear fruit.

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

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

Pearson’s chi-squared test – for categorical variables and all kinds of binomial tests. This is useful for calculating conversions (for example visitors to buyers) when you need a binomial test, such as whether a visitor to an online store made a purchase or not.

When I hear the word “distribution,” I imagine a histogram showing the frequency of occurrences of a given event.

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

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