👋 Hey guys! Today, we’re talking about how the size of a dataset impacts the sensitivity of the mean to outliers and why the median is more robust to outliers than the mean. Let’s dive in!

First, let’s look at the mean. The **mean** is the average value in a dataset. It’s calculated by adding up all the values and dividing by the number of values. 🧮 However, the mean can be very sensitive to **outliers** – extreme values in the dataset that are far away from the rest of the values. 🤯

- For example, let’s say you have a dataset of 10 numbers that are mostly between 1 and 10, but there’s one outlier that’s 100. 😱 When you calculate the mean, it’s going to be much higher than the rest of the values and not very representative of the dataset as a whole.

Now, let’s talk about the median. The **median** is the middle value in a dataset when the values are listed in order. Unlike the mean, the median is not sensitive to outliers. 🤗 It only takes into account the values in the middle of the dataset, so outliers don’t have as much of an impact.

So there you have it! 🎉 The size of the dataset can impact how sensitive the mean is to outliers, but the median is more robust and not affected by outliers. Keep learning, guys! 👨🎓👩🎓