Good job!

We have learned the following measures of central tendency:

**arithmetic mean** – the sum of all values divided by the total number of items in a dataset,
**median** – the middle point of a dataset,
**mode** – the most common value in a dataset.

Keep in mind that all these measures are **human-made**. Most measures you know from school (like the formula for the area of a triangle or the perimeter of a circle) describe how the world works: the formulas can be **verified**. Statistical measures are different: they are just **heuristics**, or experimental tools, that people found useful for summarizing data.

Let's recap what we learned. The **arithmetic mean** is the **most commonly** used measure of the three. It is important because it can be reliably estimated from the sample with inferential statistics methods.Unlike the arithmetic mean, the **median** is a **good measure of central tendency** for datasets with **extreme values**. Finally, the **mode** is the **least frequently** used method. It is mostly used for **non-numerical data**, where you can't compute the arithmetic mean or median.

Looking at the mean and median of a dataset, you can identify patterns without actually seeing the histogram:

**MEDIAN = MEAN** – the histogram is symmetric (both sides have the same number of equally distributed elements).
**MEDIAN < MEAN** – the histogram is skewed right, so the median is situated on the left side of the mean.
**MEDIAN > MEAN** – the histogram is skewed left, so the median is on the right side of the mean.