Roman's Data Science: How to monetize your data

The parameter in the general population is true, and the sample parameter is an estimate of the true parameter.

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.

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

Hypotheses typically estimate a given distribution parameter such as the mean or median. This is then used to build a histogram

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.

The advantages of bootstrapping are that it is independent from the sample distribution, the only parameters you are working with are the number of samples, and you can easily calculate any metric.

It is important to note that the p-value is not the probability that hypothesis H0 is correct. Rather, it only works towards rejecting the null hypothesis.

Bootstrapping is one of the most interesting ways to evaluate metrics in A/B tests. It is one of our favoured methods at Retail Rocket for continuous parameters such as average purchase value, average cost of goods and revenue per visitor (RPV).