What does it mean when a box plot is skewed to the right?

The mean will be about the same as the median, and the box plot will look symmetric. If the distribution is skewed to the right most values are ‘small’, but there are a few exceptionally large ones. Those exceptional values will impact the mean and pull it to the right, so that the mean will be greater than the median.

How do you compare box plot distributions?

Guidelines for comparing boxplots

1. Compare the respective medians, to compare location.
2. Compare the interquartile ranges (that is, the box lengths), to compare dispersion.
3. Look at the overall spread as shown by the adjacent values.
4. Look for signs of skewness.
5. Look for potential outliers.

How do you tell if data is skewed left or right?

A distribution that is skewed left has exactly the opposite characteristics of one that is skewed right:

1. the mean is typically less than the median;
2. the tail of the distribution is longer on the left hand side than on the right hand side; and.
3. the median is closer to the third quartile than to the first quartile.

What does a box plot tell you?

A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.

How do you interpret skewness in a histogram?

A normal distribution will have a skewness of 0. The direction of skewness is “to the tail.” The larger the number, the longer the tail. If skewness is positive, the tail on the right side of the distribution will be longer. If skewness is negative, the tail on the left side will be longer.

What is skewness a measure of?

Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution.

How do you interpret outliers in a box plot?

When reviewing a box plot, an outlier is defined as a data point that is located outside the whiskers of the box plot. For example, outside 1.5 times the interquartile range above the upper quartile and below the lower quartile (Q1 – 1.5 * IQR or Q3 + 1.5 * IQR).

What are the advantages of a box plot?

Boxplot Advantages: Summarizes variation in large datasets visually. Shows outliers. Compares multiple distributions. Indicates symmetry and skewness to a degree.