👋 **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! 👨🎓👩🎓