Skewness for Dummies
noun
pronunciation: 'skjunɪsWhat does Skewness really mean?
Skewness is a concept that helps us understand how data is distributed or spread out. It tells us if the distribution of the data is symmetrical or if it has a tendency to lean towards one side or the other. Now, let me ask you something. Have you ever played with a seesaw at a park? Just imagine that the seesaw represents our distribution of data, with equal amounts of weight on both sides, making it look balanced. That's what we call a perfectly symmetrical distribution, and it has zero skewness.
But what happens when one side of the seesaw has more weight than the other? It becomes unbalanced, right? Well, that's similar to what happens when we have skewness in our data. It means that one side of the data distribution has more values grouped together than the other side. This makes the distribution look stretched or skewed towards the side with more values.
Now, let's dive a little deeper to fully understand skewness:
When we talk about skewness, we often refer to a measure called the skewness coefficient. This coefficient tells us about the direction and the degree of skewness in our data. Now, don't worry about that term "coefficient" – it's just a fancy term for a number that helps us measure things.
So, let's imagine we have a set of data, like the heights of students in a class. If most of the students in the class are of average height, but then there are a few very tall students, our data might be skewed to the right. It means that the tail of the distribution is pointing towards the right side because the tall students pull the data in that direction. On the other hand, if we have a few very short students among a class of mostly average height, then our data might be skewed to the left.
Now, keep in mind that skewness doesn't only relate to tall or short people, it can apply to any data. For example, think about a race where most of the runners finish around the same time, but a few runners take much longer to finish. In this case, the data would be skewed towards the longer finishing times. Skewness simply tells us if one side of the data distribution is more spread out than the other side.
To summarize, skewness is a term that helps us understand if our data is balanced or if it leans towards one side. It's like a seesaw that can be perfectly balanced in the middle or become unbalanced when one side has more weight. In statistics, we use the skewness coefficient to measure the degree and direction of skewness in our data. So, keep an eye out for skewness when you're analyzing data!
But what happens when one side of the seesaw has more weight than the other? It becomes unbalanced, right? Well, that's similar to what happens when we have skewness in our data. It means that one side of the data distribution has more values grouped together than the other side. This makes the distribution look stretched or skewed towards the side with more values.
Now, let's dive a little deeper to fully understand skewness:
When we talk about skewness, we often refer to a measure called the skewness coefficient. This coefficient tells us about the direction and the degree of skewness in our data. Now, don't worry about that term "coefficient" – it's just a fancy term for a number that helps us measure things.
So, let's imagine we have a set of data, like the heights of students in a class. If most of the students in the class are of average height, but then there are a few very tall students, our data might be skewed to the right. It means that the tail of the distribution is pointing towards the right side because the tall students pull the data in that direction. On the other hand, if we have a few very short students among a class of mostly average height, then our data might be skewed to the left.
Now, keep in mind that skewness doesn't only relate to tall or short people, it can apply to any data. For example, think about a race where most of the runners finish around the same time, but a few runners take much longer to finish. In this case, the data would be skewed towards the longer finishing times. Skewness simply tells us if one side of the data distribution is more spread out than the other side.
To summarize, skewness is a term that helps us understand if our data is balanced or if it leans towards one side. It's like a seesaw that can be perfectly balanced in the middle or become unbalanced when one side has more weight. In statistics, we use the skewness coefficient to measure the degree and direction of skewness in our data. So, keep an eye out for skewness when you're analyzing data!
Revised and Fact checked by Robert Williams on 2023-10-28 18:11:48
Skewness In a sentece
Learn how to use Skewness inside a sentece
- If we have a group of students and their ages are 10, 11, 12, 13, and 20, the skewness of the ages is high because one student's age is significantly different from the others.
- In a class of 30 students, if the majority of students have a height ranging between 140cm and 160cm, but a few students are much taller or shorter, then the distribution of height has skewness.
- Imagine we have a dataset of exam scores ranging from 60 to 100. If most students scored between 70 and 80, but a few students scored very low (40-50) or very high (90-100), then the skewness of the scores is high.
- Suppose we have a company and the salaries of its employees are mostly in the range of $30,000 to $60,000, but there are a few employees earning exceptionally high salaries, like $150,000 or $200,000. This indicates a skewness in the salary distribution.
- Let's say we have a statistics class, and the majority of students scored between 70% and 80% on their final exam, but some students performed extremely poorly (below 50%) or exceptionally well (above 90%). The skewness of the exam results suggests an uneven distribution.
Skewness Synonyms
Words that can be interchanged for the original word in the same context.
Skewness Hypernyms
Words that are more generic than the original word.