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When we look at a dataset, we often use numbers like the mean and median to describe the "center" or "average" of that data.
Both the mean and median help to summarize the data, but they do it in slightly different ways.

The **mean** is what many people think of as the "average." It's calculated by adding up all the numbers in a set and then dividing by how many numbers there are. However, the mean can be greatly influenced by extremely high or low numbers, known as outliers.

• This susceptibility to outliers might make the mean not truly representative of the dataset.

On the other hand, the **median** is the middle number in a sorted list of numbers.
It's the point where half the numbers are higher and half are lower.

• The median isn't affected by outliers because it relies purely on position in the data, making it a robust measure, especially in skewed distributions.

In a right-skewed distribution, where there are a few very high values, the mean gets "pulled" to the right by those large values, ending up higher than the median.

Data asymmetry refers to how data points in a dataset are not evenly distributed on either side of a central point like the mean.
In symmetric distributions, values are spread evenly, looking the same on the left and right of the center.

However, in many real-world datasets, perfect symmetry is rare.

• Right-skewed distributions have a "tail" that extends more widely to the right.
• Left-skewed distributions extend more widely to the left.

Understanding whether data is symmetric or asymmetric helps us decide on what summary statistics to use.
For example, in the case of the teen friendship survey, the 43% of teens who had zero online friends heavily influences the shape of the distribution.
This causes the values to cluster heavily on the left while the right "tail" stretches due to teens who made many online friends, indicating data asymmetry.

Right skewness, also referred to as positive skewness, occurs when the majority of data points cluster to the left, with the tail extending to the right.

In the context of the teen survey about online friends, 43% of respondents reported making no online friends, resulting in many values at zero or low numbers. However, since some teens reported a high number of online friends, these few higher values create a long tail on the right.

• This right-skewed nature suggests looking closer at data points on the extreme high side for insights.

Right-skewed distributions have some important characteristics:

• They often result in the mean being higher than the median because of the impact of the "tail" that's pulling the mean upwards.
• They can indicate that there are outliers or uncommon events worth investigating separately.

Understanding skewness, particularly right skewness, can signal when extra caution is needed in interpreting data averages like the mean.