Understanding Skewed Histograms in Data Analysis

What is a skewed histogram?

A histogram with a concentration of either low values or high values is called...

Answer:

A histogram with a concentration of either low or high values, which can be seen in the shape of the histogram, is referred to as skewed.

A skewed histogram is a type of graphical representation of data that shows a non-symmetrical distribution of values. It indicates an imbalance in the dataset, with more values clustered towards one end of the range. This imbalance can be observed visually by the shape of the histogram.

There are two main types of skewed histograms: left skewed and right skewed. A left skewed histogram, also known as negatively skewed, has a concentration of data on the left (low) end of the distribution, causing the histogram to lean towards the right. On the other hand, a right skewed histogram, known as positively skewed, shows a concentration of data on the right (high) end, making the histogram lean towards the left.

Understanding skewed histograms is crucial in data analysis as it provides insights into the distribution of values and helps in identifying patterns or anomalies in the dataset. By analyzing the skewness of a histogram, data analysts can make informed decisions and draw meaningful conclusions from the data.