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

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

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).

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

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.

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

One problem with all of these tests is that they are distribution-specific. For example, the Student’s t-test and the z-test require normally distributed data.

In data analysis, survival bias is taking the known into account while neglecting the unknown (which nevertheless exists).

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).