Roman's Data Science: How to monetize your data

In A/B tests, we work with two groups – a test group and a control group. Both need their own bootstrap.

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

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

Bootstrapping [79] works by using multiple samples from the data that are then used to calculate statistics.

Worried about losing the tender, the other company fudged the results by moving users who were close to making a purchase (for example, those who had already added an item to their carts) to their own system. They weren’t doing this all the time, only on certain days and at certain times.

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.

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.

First, we need to know what the goal of the hypothesis is – what quantitative metric will it optimize?

Z-test – for checking the mean of a normally distributed quantity. Student’s t-test – the same as a z-test, but for small samples (t < 100).

Statistical hypothesis testing involves two important concepts: general population and sample.